From a77f70b00072746dc3c7ae32d2046af6504739be Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Wed, 14 Dec 2022 20:46:26 +0000
Subject: [PATCH 01/52] init

---
 index.rst                                |   8 +
 intermediate_source/reinforcement_ppo.py | 385 +++++++++++++++++++++++
 2 files changed, 393 insertions(+)
 create mode 100644 intermediate_source/reinforcement_ppo.py

diff --git a/index.rst b/index.rst
index 0e2f1eaeaa6..cca477d9068 100644
--- a/index.rst
+++ b/index.rst
@@ -269,6 +269,13 @@ What's new in PyTorch tutorials?
    :link: intermediate/reinforcement_q_learning.html
    :tags: Reinforcement-Learning
 
+.. customcarditem::
+   :header: Reinforcement Learning (PPO) with TorchRL
+   :card_description: Learn how to use PyTorch and TorchRL to train a Proximal Policy Optimization agent on the cheetah-run task from dm-control.
+   :image: _static/img/cheetah.gif
+   :link: intermediate/reinforcement_ppo.html
+   :tags: Reinforcement-Learning
+
 .. customcarditem::
    :header: Train a Mario-playing RL Agent
    :card_description: Use PyTorch to train a Double Q-learning agent to play Mario.
@@ -839,6 +846,7 @@ Additional Resources
    :caption: Reinforcement Learning
 
    intermediate/reinforcement_q_learning
+   intermediate/reinforcement_ppo
    intermediate/mario_rl_tutorial
 
 .. toctree::
diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
new file mode 100644
index 00000000000..9c68900472d
--- /dev/null
+++ b/intermediate_source/reinforcement_ppo.py
@@ -0,0 +1,385 @@
+# -*- coding: utf-8 -*-
+"""
+Reinforcement Learning (PPO) Tutorial
+=====================================
+**Author**: `Vincent Moens <https://github.com/vmoens>`_
+
+.. code-block:: bash
+
+   %%bash
+   pip3 install git+https://github.com/pytorch/rl.git
+   pip3 install dm_control
+   pip3 install tqdm
+
+Proximal Policy Optimization (PPO) is a policy-gradient algorithm (think of it
+as an elaborated version of REINFORCE) where after a batch of data has been
+collected, it is used in an inner loop to optimize the policy to maximise
+the expected return given some constraints.
+
+Ref: https://arxiv.org/abs/1707.06347
+
+In this tutorial, you will learn how to use TorchRL to code a PPO algorithm,
+including instructions on how to collect data, run multiple environments in parallel
+or normalize the data.
+
+PPO is usually regarded as a fast and efficient method online reinforcement
+algorithm.
+The algorithm works as follows: we will sample a batch of data by playing the
+policy in the environment for a given number of steps. Then, we will perform a
+given number of optimization steps with random sub-samples of this batch using
+a clipped version of the REINFORCE loss.
+The clipping will put a pessimistic bound on our loss.
+The precise formula of the loss is:
+
+.. math::
+  L(s,a,\theta_k,\theta) = \min\left(
+\frac{\pi_{\theta}(a|s)}{\pi_{\theta_k}(a|s)}  A^{\pi_{\theta_k}}(s,a), \;\;
+g(\epsilon, A^{\pi_{\theta_k}}(s,a))
+\right),
+
+There are two components in that loss: in the first part of the minimum operator,
+we simply compute an importance-weighted version of the REINFORCE loss (i.e. a
+REINFORCE loss that we have corrected for the fact that the current policy
+configuration lags the one that was used for the data collection).
+The second part of that minimum operator is a similar loss where we have clipped
+the ratios when they exceeded or were below a given pair of thresholds.
+
+This loss ensures that whether the advantage is positive or negative, policy
+updates that would produce significant shifts from the previous configuration
+are being discouraged.
+
+"""
+import math
+
+import matplotlib.pyplot as plt
+import torch
+from tensordict.nn import TensorDictModule
+from torch import nn
+from torchrl.collectors import MultiSyncDataCollector
+from torchrl.envs import ParallelEnv, TransformedEnv, ObservationNorm, Compose, \
+    CatTensors, DoubleToFloat, EnvCreator, CatFrames
+from torchrl.envs.libs.dm_control import DMControlEnv
+from torchrl.envs.utils import set_exploration_mode
+from torchrl.modules import NormalParamWrapper, TanhNormal, ProbabilisticActor, ValueOperator
+from torchrl.objectives.value import GAE
+from torchrl.objectives.utils import distance_loss
+from torchrl.data.replay_buffers.storages import LazyTensorStorage
+from torchrl.data.replay_buffers.rb_prototype import ReplayBuffer
+from tqdm import tqdm
+
+######################################################################
+# Hyperparameters
+# ---------------
+#
+# We set the hyperparameters for our algorithm. Depending on the resources
+# available, one may choose to execute the policy on CUDA or on another
+# device.
+# The :obj:`frame_skip` will control how for how many frames is a single
+# action being executed. The rest of the arguments that count frames
+# must be corrected for this value (since one environment step will
+# actually return :obj:`frame_skip` frames).
+#
+num_procs = 4
+num_cells = 128
+frame_skip = 2
+frames_per_batch = 2000 // frame_skip
+max_frames_per_traj = 1000 // frame_skip
+total_frames = 1000000 // frame_skip
+device = "cpu"
+gamma = 0.99
+lmbda = 0.95
+batch_size = 64  # total batch-size to compute the loss
+num_epochs = 10  # optimisation steps per batch of data collected
+clip_epsilon = 0.2  # clip value for PPO loss
+lr_policy = 3e-4
+lr_value = 1e-3
+max_grad_norm = 1.0
+n_entropy_samples = 10
+entropy_eps = 1e-3
+seed = 0
+
+
+######################################################################
+# Define a parallel environment
+# -----------------------------
+#
+# A parallel environment is defined with a custom function that returns an environment
+# instance and a number of workers to spawn. The environment creator function should be
+# wrapped in an :obj:`EnvCreator` instance to keep track of the meta-data.
+# For this task, we'll focus on the cheetah-run task from the DeepMind control suite
+# (https://linkinghub.elsevier.com/retrieve/pii/S2665963820300099)
+#
+torch.manual_seed(seed)
+
+def env_maker(from_pixels=False):
+    return DMControlEnv("cheetah", "run", device=device,
+                        frame_skip=frame_skip, from_pixels=from_pixels)
+
+
+parallel_env = ParallelEnv(num_procs, EnvCreator(env_maker))
+parallel_env.set_seed(seed)
+
+######################################################################
+# Transforms
+# ----------
+#
+# We will append some transforms to our environments to prepare the data for
+# the policy. The :obj:`CatTensors` transform will concatenate the two observation
+# keys ("position" and "velocity") into a single state named "observation".
+# We will then normalize the data. In general, it is preferable to have data
+# that loosely match a unit Gaussian distribution: to obtain this, we will
+# run a certain number of random steps in the environment and compute
+# the summary statistics of these observations.
+# Since we're executing the environments in parallel, we can execute this
+# normalization in batch.
+# Finally, dm_control returns values in float64 and expects the actions to
+# be in the same format. Since our network is parameterized with single
+# precision numbers, we must transform the actions to float64 and then transform
+# the reward and the observation back to float32. The :obj:`DoubleToFloat`
+# has, as all TorchRL transforms do, a forward and an inverse method.
+# The inverse is being called before the environment step is executed, the
+# forward after. Hence, we just need to specify which entry has to be
+# transformed when the data (i.e. the action) gets in and which when the
+# data (i.e. the observation) is returned.
+#
+
+env = TransformedEnv(
+    parallel_env,
+    Compose(
+        CatTensors(["position", "velocity"], "observation"),
+        # concatenates the observations in a single vector
+        ObservationNorm(in_keys=["observation"]),
+        # dm_control returns float64 observations and expects float64 actions
+        DoubleToFloat(in_keys=["observation", "reward"],
+                      in_keys_inv=["action"]),
+    )
+)
+
+######################################################################
+# :obj:`ObservationNorm` can automatically gather the summary statistics of
+# our environment:
+#
+env.transform[1].init_stats(max_frames_per_traj // frame_skip * env.batch_size[0], (0, 1), 1)
+# check the shape of our summary stats
+print("normalization constant shape:", env.transform[1].loc.shape)
+
+######################################################################
+# We create an evaluation environment to check the policy performance in
+# pure exploitation mode:
+#
+eval_env = TransformedEnv(
+    ParallelEnv(
+        num_procs,
+        EnvCreator(lambda: env_maker(from_pixels=True))
+    ),
+    env.transform.clone())
+assert (eval_env.transform[1].loc == env.transform[1].loc).all()
+
+######################################################################
+# Policy
+# ------
+#
+# PPO utilizes a stochastic policy to handle exploration.
+# As the data is continuous, we use a Tanh-Normal distribution to respect the
+# distribution bounds.
+# We design the policy in three steps:
+# 1. Define a neural network D_obs -> 2 D_action
+# 2. Wrap this neural network in a NormalParamWrapper to extract a location and a scale
+# 3. Create a probabilistic TensorDictModule that can create this distribution and sample from it.
+#
+
+actor_net = nn.Sequential(
+    nn.LazyLinear(num_cells),
+    nn.Tanh(),
+    nn.LazyLinear(num_cells),
+    nn.Tanh(),
+    nn.LazyLinear(2 * env.action_spec.shape[-1]),
+)
+value_net = nn.Sequential(
+    nn.LazyLinear(num_cells),
+    nn.Tanh(),
+    nn.LazyLinear(num_cells),
+    nn.Tanh(),
+    nn.LazyLinear(1),
+)
+
+######################################################################
+# The output of the network must be a location and a non-negative scale.
+# The NormalParamWrapper class does just that transformation:
+#
+actor_net = NormalParamWrapper(actor_net)
+policy_module = TensorDictModule(actor_net, in_keys=["observation"],
+                                 out_keys=["loc", "scale"])
+policy_module = ProbabilisticActor(
+    module=policy_module,
+    spec=env.action_spec,
+    in_keys=["loc", "scale"],
+    distribution_class=TanhNormal,
+    return_log_prob=True,
+)
+value_module = ValueOperator(
+    module=value_net,
+    in_keys=["observation"],
+)
+
+######################################################################
+# let's try our policy:
+#
+print("Running policy:", policy_module(env.reset()))
+print("Running value:", value_module(env.reset()))
+
+######################################################################
+# Data collector
+#
+# TorchRL provides a set of DataCollector classes. Briefly, these classes
+# allow you to control how many frames to collect at each iteration,
+# when to reset the environment, on which device the policy should be executed etc.
+
+collector = MultiSyncDataCollector(
+    # we'll just run one ParallelEnvironment. Adding elements to the list would increase the number of envs run in parallel
+    [env,],
+    policy_module,
+    frames_per_batch=frames_per_batch,
+    max_frames_per_traj=max_frames_per_traj,
+    total_frames=total_frames,
+)
+collector.set_seed(seed)
+
+######################################################################
+# Replay buffer
+#
+# We store the data in a replay buffer after every collection
+
+replay_buffer = ReplayBuffer(
+    storage=LazyTensorStorage(frames_per_batch),
+    prefetch=10,
+)
+
+######################################################################
+# Loss function
+# -------------
+#
+# The PPO loss can be directly imported from torchrl for convenience using
+#
+# .. code-block:: python
+#
+#    from torchrl.objectives import PPOClipLoss
+#    loss_module = ClipPPOLoss(policy, critic, gamma=gamma)
+#
+# For a more explicit description, we unwrap this code in the training loop.
+# Simply put, the objective function of PPO consists in maximising the expected
+# value of an advantage function for the policy parameters given some proximity
+# constraints. Let us first build the advantage module:
+#
+
+advantage_module = GAE(gamma=gamma, lmbda=lmbda, value_network=value_module)
+
+# ######################################################################
+# # Reward normalization
+# # --------------------
+# #
+# # To make training more efficient, we normalize the rewards online for the empirical
+# # distribution to approximately match a unit Gaussian.
+# # This somewhat guarantees that our choice of hyperparameter is more robust
+# # to the specifics of the environent we are using.
+# #
+# reward_normalizer = RewardNormalizer(decay=0.9999, scale=0.1)
+
+######################################################################
+# Training loop
+# -------------
+#
+
+optim_policy = torch.optim.Adam(policy_module.parameters(), lr_policy)
+optim_value = torch.optim.Adam(value_module.parameters(), lr_value)
+scheduler_policy = torch.optim.lr_scheduler.CosineAnnealingLR(optim_policy, total_frames // frames_per_batch, lr_policy/2)
+scheduler_value = torch.optim.lr_scheduler.CosineAnnealingLR(optim_value, total_frames // frames_per_batch, lr_value/2)
+
+logs = []
+logs_eval_rewards = []
+log_clip_bounds = (math.log1p(-clip_epsilon), math.log1p(clip_epsilon))
+pbar = tqdm(total=total_frames * frame_skip)
+eval_str = ""
+for i, data in enumerate(collector):
+    logs.append(data["reward"].sum(-2).mean().item())
+    pbar.update(data["mask"].sum().item() * frame_skip)
+    cum_reward_str = f"cumulative reward={logs[-1]: 4.4f} (init={logs[0]: 4.4f})"
+    lr_str = f"lr policy: {optim_policy.param_groups[0]['lr']: 4.4f}"
+    pbar.set_description(", ".join([
+        eval_str,
+        cum_reward_str,
+        lr_str
+    ]))
+    # reward_normalizer.update_reward_stats(data)
+    # reward_normalizer.normalize_reward(data)
+    for k in range(num_epochs):
+        advantage_module(data)
+        advantage = data["advantage"]
+        data["advantage"] = (advantage - advantage.mean()) / advantage.std().clamp_min(1e-3)
+        # we place the data in the replay buffer after removing the time dimension
+        replay_buffer.extend(data.view(-1))
+        for j in range(frames_per_batch // batch_size):
+            subdata, *_ = replay_buffer.sample(batch_size)
+            # loss (1): Objective
+            action = subdata.get("action")
+            advantage = subdata.get("advantage")
+            dist = policy_module.get_dist(subdata.clone(recurse=False))
+            log_prob = dist.log_prob(action)
+            log_prob = log_prob.unsqueeze(-1)
+            prev_log_prob = subdata.get("sample_log_prob")
+            log_weight = log_prob - prev_log_prob
+
+            gain1 = log_weight.exp() * advantage
+            log_weight_clip = torch.empty_like(log_weight)
+            idx_pos = advantage >= 0
+            log_weight_clip[idx_pos] = log_weight[idx_pos].clamp_max(log_clip_bounds[1])
+            log_weight_clip[~idx_pos] = log_weight[~idx_pos].clamp_min(log_clip_bounds[0])
+
+            gain2 = log_weight_clip.exp() * advantage
+            gain = torch.stack([gain1, gain2], -1).min(dim=-1)[0]
+            loss_objective = -gain.mean()
+
+            # Entropy bonus: we add a small entropy bonus to favour exploration
+            entropy_bonus = entropy_eps * dist.log_prob(dist.rsample((n_entropy_samples,))).mean()
+
+            # Optim
+            loss = loss_objective + entropy_bonus
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(optim_policy.param_groups[0]["params"], max_grad_norm)
+            optim_policy.step()
+            optim_policy.zero_grad()
+
+            # loss (2): Value.
+            # A simpler implementation would be to use GAE(..., gradient_mode=True), which
+            # would compute a differentiable "value_error" key-value pair to be used for the
+            # value network training.
+            value_module(subdata)
+            value = subdata.get("state_value")
+            advantage_diff = value - subdata["value_target"]
+            loss_value = distance_loss(
+                advantage_diff,
+                torch.zeros_like(advantage_diff),
+                loss_function="l2",
+            ).mean()
+            # Optim
+            loss_value.backward()
+            torch.nn.utils.clip_grad_norm_(optim_value.param_groups[0]["params"], max_grad_norm)
+            optim_value.step()
+            optim_value.zero_grad()
+
+
+    collector.update_policy_weights_()
+    scheduler_policy.step()
+    scheduler_value.step()
+    if i % 10 == 0:
+        with set_exploration_mode("mean"), torch.no_grad():
+            eval_rollout = eval_env.rollout(max_frames_per_traj//frame_skip, policy_module)
+            eval_reward = eval_rollout["reward"].sum(-2).mean().item()
+            logs_eval_rewards.append(eval_reward)
+            eval_str = f"eval cumulative reward: {logs_eval_rewards[-1]: 4.4f} (init: {logs_eval_rewards[0]: 4.4f})"
+            print(eval_rollout)
+
+plt.plot(logs)
+plt.savefig("training.png")
+collector.shutdown()
+del collector

From d45c894481bfdc3650616bf9f6d11b6af0c6d1b3 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Wed, 14 Dec 2022 22:32:00 +0000
Subject: [PATCH 02/52] edits

---
 intermediate_source/reinforcement_ppo.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 9c68900472d..4e4860212cb 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -7,7 +7,7 @@
 .. code-block:: bash
 
    %%bash
-   pip3 install git+https://github.com/pytorch/rl.git
+   pip3 install torchrl
    pip3 install dm_control
    pip3 install tqdm
 
@@ -32,6 +32,7 @@
 The precise formula of the loss is:
 
 .. math::
+
   L(s,a,\theta_k,\theta) = \min\left(
 \frac{\pi_{\theta}(a|s)}{\pi_{\theta_k}(a|s)}  A^{\pi_{\theta_k}}(s,a), \;\;
 g(\epsilon, A^{\pi_{\theta_k}}(s,a))

From 160706783ddd04a19ca09200cf61ec8eeb36df70 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 3 Jan 2023 12:34:12 +0000
Subject: [PATCH 03/52] edits

---
 intermediate_source/reinforcement_ppo.py | 53 ++++++++++++------------
 requirements.txt                         |  1 +
 2 files changed, 28 insertions(+), 26 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 4e4860212cb..bde4801703d 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -1,9 +1,19 @@
 # -*- coding: utf-8 -*-
 """
-Reinforcement Learning (PPO) Tutorial
-=====================================
+Reinforcement Learning (PPO) with TorchRL Tutorial
+==================================================
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
+This tutorial shows how to use PyTorch and TorchRL to train a parametric policy
+network to solve the cheetah-run task from the `DeepMind control library
+<https://github.com/deepmind/dm_control>`__.
+
+Key learning items:
+- What is PPO and how to code the loss;
+- How to create an environment in TorchRL, transform its outputs, and collect data from this env;
+- How to compute the advantage signal for policy gradient methods;
+- How to create a dynamic replay buffer and sample from it without repetition.
+
 .. code-block:: bash
 
    %%bash
@@ -99,7 +109,6 @@
 entropy_eps = 1e-3
 seed = 0
 
-
 ######################################################################
 # Define a parallel environment
 # -----------------------------
@@ -270,21 +279,14 @@ def env_maker(from_pixels=False):
 # For a more explicit description, we unwrap this code in the training loop.
 # Simply put, the objective function of PPO consists in maximising the expected
 # value of an advantage function for the policy parameters given some proximity
-# constraints. Let us first build the advantage module:
-#
+# constraints. Let us first build the advantage module. PPO usually uses the GAE
+# (Generalized Advantage Estimator) function (https://arxiv.org/abs/1506.02438).
+# TorchRL provides a vectorized implementation of this function.
+# The GAE module will update the input tensordict with new "advantage" and "value_target" keys.
+# The value_target is a gradient-free tensor that represents the empirical
+# value that the value network should represent with the input observation.
 
-advantage_module = GAE(gamma=gamma, lmbda=lmbda, value_network=value_module)
-
-# ######################################################################
-# # Reward normalization
-# # --------------------
-# #
-# # To make training more efficient, we normalize the rewards online for the empirical
-# # distribution to approximately match a unit Gaussian.
-# # This somewhat guarantees that our choice of hyperparameter is more robust
-# # to the specifics of the environent we are using.
-# #
-# reward_normalizer = RewardNormalizer(decay=0.9999, scale=0.1)
+advantage_module = GAE(gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True)
 
 ######################################################################
 # Training loop
@@ -311,14 +313,12 @@ def env_maker(from_pixels=False):
         cum_reward_str,
         lr_str
     ]))
-    # reward_normalizer.update_reward_stats(data)
-    # reward_normalizer.normalize_reward(data)
     for k in range(num_epochs):
-        advantage_module(data)
-        advantage = data["advantage"]
-        data["advantage"] = (advantage - advantage.mean()) / advantage.std().clamp_min(1e-3)
         # we place the data in the replay buffer after removing the time dimension
-        replay_buffer.extend(data.view(-1))
+        # the "mask" key represents the valid data in the batch: by indexing with "mask"
+        # we make sure that we eliminate all the 0-padding values.
+        advantage_module(data)
+        replay_buffer.extend(data[data["mask"]])
         for j in range(frames_per_batch // batch_size):
             subdata, *_ = replay_buffer.sample(batch_size)
             # loss (1): Objective
@@ -326,9 +326,11 @@ def env_maker(from_pixels=False):
             advantage = subdata.get("advantage")
             dist = policy_module.get_dist(subdata.clone(recurse=False))
             log_prob = dist.log_prob(action)
-            log_prob = log_prob.unsqueeze(-1)
             prev_log_prob = subdata.get("sample_log_prob")
-            log_weight = log_prob - prev_log_prob
+            # we need to unsqueeze the log_weight for the last dim, as
+            # the advantage has shape [batch, 1] but the log-probability has
+            # just size [batch]
+            log_weight = (log_prob - prev_log_prob).unsqueeze(-1)
 
             gain1 = log_weight.exp() * advantage
             log_weight_clip = torch.empty_like(log_weight)
@@ -378,7 +380,6 @@ def env_maker(from_pixels=False):
             eval_reward = eval_rollout["reward"].sum(-2).mean().item()
             logs_eval_rewards.append(eval_reward)
             eval_str = f"eval cumulative reward: {logs_eval_rewards[-1]: 4.4f} (init: {logs_eval_rewards[0]: 4.4f})"
-            print(eval_rollout)
 
 plt.plot(logs)
 plt.savefig("training.png")
diff --git a/requirements.txt b/requirements.txt
index 7f6760428f5..9eefeb21c43 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,6 +25,7 @@ tensorboard
 jinja2==3.0.3
 pytorch-lightning
 torchx
+torchrl
 ax-platform
 nbformat>=4.2.0
 datasets

From 4527f99b6d40fb4e5a8edfe93ad210898cf97463 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 3 Jan 2023 12:46:42 +0000
Subject: [PATCH 04/52] edits

---
 intermediate_source/reinforcement_ppo.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index bde4801703d..b0c688ae617 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -12,6 +12,7 @@
 - What is PPO and how to code the loss;
 - How to create an environment in TorchRL, transform its outputs, and collect data from this env;
 - How to compute the advantage signal for policy gradient methods;
+- How to create a stochastic policy using a probabilistic neural network;
 - How to create a dynamic replay buffer and sample from it without repetition.
 
 .. code-block:: bash
@@ -220,12 +221,20 @@ def env_maker(from_pixels=False):
 actor_net = NormalParamWrapper(actor_net)
 policy_module = TensorDictModule(actor_net, in_keys=["observation"],
                                  out_keys=["loc", "scale"])
+
+######################################################################
+# We now need to build a distribution out of the location and scale of our normal
+# distribution. As the action space is bounded, we'll use a TanhNormal distribution
+# to limit the values of the samples withing the accepted range. Here's how to code
+# this in TorchRL:
+#
 policy_module = ProbabilisticActor(
     module=policy_module,
     spec=env.action_spec,
     in_keys=["loc", "scale"],
     distribution_class=TanhNormal,
-    return_log_prob=True,
+    distribution_kwargs={"min": env.action_spec.space.minimum, "max": env.action_spec.space.maximum},
+    return_log_prob=True,  # we'll need the log-prob for the numerator of the importance weights
 )
 value_module = ValueOperator(
     module=value_net,

From 9aff0c238d8b6c91d7658ec2b7986887d3af3d0b Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 3 Jan 2023 13:22:59 +0000
Subject: [PATCH 05/52] edits

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 requirements.txt                         | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index b0c688ae617..5336afbc141 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -107,7 +107,7 @@
 lr_value = 1e-3
 max_grad_norm = 1.0
 n_entropy_samples = 10
-entropy_eps = 1e-3
+entropy_eps = 1e-4
 seed = 0
 
 ######################################################################
diff --git a/requirements.txt b/requirements.txt
index 9eefeb21c43..7cf18597874 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -26,6 +26,7 @@ jinja2==3.0.3
 pytorch-lightning
 torchx
 torchrl
+dm_control
 ax-platform
 nbformat>=4.2.0
 datasets

From ba1a5bf05f8b9eb2ae5c429e7b5ca7e89ddc6265 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 3 Jan 2023 16:55:45 +0000
Subject: [PATCH 06/52] edits

---
 intermediate_source/reinforcement_ppo.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 5336afbc141..ad10a913dd0 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -76,6 +76,7 @@
 from torchrl.objectives.value import GAE
 from torchrl.objectives.utils import distance_loss
 from torchrl.data.replay_buffers.storages import LazyTensorStorage
+from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement
 from torchrl.data.replay_buffers.rb_prototype import ReplayBuffer
 from tqdm import tqdm
 
@@ -194,7 +195,7 @@ def env_maker(from_pixels=False):
 # As the data is continuous, we use a Tanh-Normal distribution to respect the
 # distribution bounds.
 # We design the policy in three steps:
-# 1. Define a neural network D_obs -> 2 D_action
+# 1. Define a neural network D_obs -> 2 D_action (loc and scale both have dimension D_action)
 # 2. Wrap this neural network in a NormalParamWrapper to extract a location and a scale
 # 3. Create a probabilistic TensorDictModule that can create this distribution and sample from it.
 #
@@ -271,7 +272,7 @@ def env_maker(from_pixels=False):
 
 replay_buffer = ReplayBuffer(
     storage=LazyTensorStorage(frames_per_batch),
-    prefetch=10,
+    sampler=SamplerWithoutReplacement(),
 )
 
 ######################################################################
@@ -309,6 +310,7 @@ def env_maker(from_pixels=False):
 
 logs = []
 logs_eval_rewards = []
+# log-ratio bounds: log(1-eps) and log(1+eps)
 log_clip_bounds = (math.log1p(-clip_epsilon), math.log1p(clip_epsilon))
 pbar = tqdm(total=total_frames * frame_skip)
 eval_str = ""
@@ -342,12 +344,10 @@ def env_maker(from_pixels=False):
             log_weight = (log_prob - prev_log_prob).unsqueeze(-1)
 
             gain1 = log_weight.exp() * advantage
-            log_weight_clip = torch.empty_like(log_weight)
-            idx_pos = advantage >= 0
-            log_weight_clip[idx_pos] = log_weight[idx_pos].clamp_max(log_clip_bounds[1])
-            log_weight_clip[~idx_pos] = log_weight[~idx_pos].clamp_min(log_clip_bounds[0])
 
+            log_weight_clip = log_weight.clamp(*log_clip_bounds)
             gain2 = log_weight_clip.exp() * advantage
+
             gain = torch.stack([gain1, gain2], -1).min(dim=-1)[0]
             loss_objective = -gain.mean()
 
@@ -367,12 +367,12 @@ def env_maker(from_pixels=False):
             # value network training.
             value_module(subdata)
             value = subdata.get("state_value")
-            advantage_diff = value - subdata["value_target"]
             loss_value = distance_loss(
-                advantage_diff,
-                torch.zeros_like(advantage_diff),
-                loss_function="l2",
+                value,
+                subdata["value_target"],
+                loss_function="smooth_l1",
             ).mean()
+
             # Optim
             loss_value.backward()
             torch.nn.utils.clip_grad_norm_(optim_value.param_groups[0]["params"], max_grad_norm)

From 54e75e92f2ed450d9561b3deab80b537623422e4 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Wed, 4 Jan 2023 15:19:33 +0000
Subject: [PATCH 07/52] edits

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 7cf18597874..6f98bb43833 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,7 +25,7 @@ tensorboard
 jinja2==3.0.3
 pytorch-lightning
 torchx
-torchrl
+torchrl-nightly  # temporary
 dm_control
 ax-platform
 nbformat>=4.2.0

From 5c67fc139a03e5d270013d12620c84fc39cdf418 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Wed, 4 Jan 2023 17:31:14 +0000
Subject: [PATCH 08/52] edits

---
 intermediate_source/reinforcement_ppo.py | 5 ++++-
 requirements.txt                         | 3 ++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index ad10a913dd0..bf60071451c 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -61,6 +61,9 @@
 are being discouraged.
 
 """
+import os
+os.environ["MUJOCO_GL"] = "egl"
+
 import math
 
 import matplotlib.pyplot as plt
@@ -97,7 +100,7 @@
 frame_skip = 2
 frames_per_batch = 2000 // frame_skip
 max_frames_per_traj = 1000 // frame_skip
-total_frames = 1000000 // frame_skip
+total_frames = 100_000 // frame_skip
 device = "cpu"
 gamma = 0.99
 lmbda = 0.95
diff --git a/requirements.txt b/requirements.txt
index 6f98bb43833..bfee5437f85 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -26,7 +26,6 @@ jinja2==3.0.3
 pytorch-lightning
 torchx
 torchrl-nightly  # temporary
-dm_control
 ax-platform
 nbformat>=4.2.0
 datasets
@@ -54,6 +53,8 @@ pillow==9.3.0
 wget
 gym==0.25.1
 gym-super-mario-bros==7.4.0
+dm_control
+pyopengl
 gymnasium==0.27.0
 timm
 iopath

From 8a3951b5699fa12c15a099cce40c0d316ea428e8 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 5 Jan 2023 17:15:49 +0000
Subject: [PATCH 09/52] no rendering

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index bf60071451c..16bf32bb32e 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -185,7 +185,7 @@ def env_maker(from_pixels=False):
 eval_env = TransformedEnv(
     ParallelEnv(
         num_procs,
-        EnvCreator(lambda: env_maker(from_pixels=True))
+        EnvCreator(lambda: env_maker(from_pixels=False))
     ),
     env.transform.clone())
 assert (eval_env.transform[1].loc == env.transform[1].loc).all()

From 059d2bcb9f8b7d5c17d007f0d2c36956e234149f Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 14:29:56 +0000
Subject: [PATCH 10/52] refining

---
 intermediate_source/reinforcement_ppo.py | 520 ++++++++++++++---------
 1 file changed, 327 insertions(+), 193 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 16bf32bb32e..2589a488c1f 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -5,41 +5,47 @@
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
 This tutorial shows how to use PyTorch and TorchRL to train a parametric policy
-network to solve the cheetah-run task from the `DeepMind control library
-<https://github.com/deepmind/dm_control>`__.
+network to solve the Ant task from the OpenAI-Gym/Farama-Gymnasium control library
+<https://github.com/Farama-Foundation/Gymnasium>`__.
 
 Key learning items:
-- What is PPO and how to code the loss;
 - How to create an environment in TorchRL, transform its outputs, and collect data from this env;
-- How to compute the advantage signal for policy gradient methods;
-- How to create a stochastic policy using a probabilistic neural network;
-- How to create a dynamic replay buffer and sample from it without repetition.
+- How to make your classes talk to each other using :class:`TensorDict`;
+- The basics of building your training loop with TorchRL:
+  - How to compute the advantage signal for policy gradient methods;
+  - How to create a stochastic policy using a probabilistic neural network;
+  - How to create a dynamic replay buffer and sample from it without repetition.
+
+We will cover six crucial components of TorchRL: environments, transforms,
+models (policy and value function), loss modules, data collectors and replay buffers.
 
 .. code-block:: bash
 
    %%bash
    pip3 install torchrl
-   pip3 install dm_control
+   pip3 install gym[mujoco]
    pip3 install tqdm
 
 Proximal Policy Optimization (PPO) is a policy-gradient algorithm (think of it
-as an elaborated version of REINFORCE) where after a batch of data has been
-collected, it is used in an inner loop to optimize the policy to maximise
-the expected return given some constraints.
+as an elaborated version of REINFORCE) where a batch of data is being
+collected and directly consumed to train the policy to maximise
+the expected return given some proximality constraints.
 
 Ref: https://arxiv.org/abs/1707.06347
 
-In this tutorial, you will learn how to use TorchRL to code a PPO algorithm,
-including instructions on how to collect data, run multiple environments in parallel
-or normalize the data.
+PPO is usually regarded as a fast and efficient method for online, on-policy
+reinforcement algorithm. TorchRL provides a loss-module that does all the work
+for you, so that you can rely on this implementation and focus on solving your
+problem rather than re-inventing the wheel every time you want to train a policy.
 
-PPO is usually regarded as a fast and efficient method online reinforcement
-algorithm.
+For completeness, here is a brief overview of what the loss computes, even though
+this is taken care of by our :class:`ClipPPOLoss` module:
 The algorithm works as follows: we will sample a batch of data by playing the
 policy in the environment for a given number of steps. Then, we will perform a
 given number of optimization steps with random sub-samples of this batch using
 a clipped version of the REINFORCE loss.
-The clipping will put a pessimistic bound on our loss.
+The clipping will put a pessimistic bound on our loss: lower return estimates will
+be favoured compared to higher ones.
 The precise formula of the loss is:
 
 .. math::
@@ -60,27 +66,41 @@
 updates that would produce significant shifts from the previous configuration
 are being discouraged.
 
+This tutorial is structured as follows: first, we will define the set of hyperparameters
+we will be using for training.
+Next, we will focus on creating our environment, or simulator, using TorchRL's
+wrappers and transforms. We will design the policy network and the value model,
+which is indispensable to the loss function. These modules will be used to configure
+our loss module, and finally we will be creating the replay buffer and data loader.
+
+Once all these elements are on stage, we will be able to run our training loop.
+
+Throughout this tutorial, we'll be using the :py:mod:`tensordict` library. :class:`TensorDict`
+is the lingua franca of TorchRL: it helps us abstract what a module reads and writes
+and care less about the specific data description and more about the algorithm itself.
+
 """
-import os
-os.environ["MUJOCO_GL"] = "egl"
 
 import math
+from collections import defaultdict
 
 import matplotlib.pyplot as plt
 import torch
 from tensordict.nn import TensorDictModule
+from tensordict.nn.distributions import NormalParamExtractor
 from torch import nn
-from torchrl.collectors import MultiSyncDataCollector
+from torchrl.collectors import SyncDataCollector
 from torchrl.envs import ParallelEnv, TransformedEnv, ObservationNorm, Compose, \
-    CatTensors, DoubleToFloat, EnvCreator, CatFrames
-from torchrl.envs.libs.dm_control import DMControlEnv
-from torchrl.envs.utils import set_exploration_mode
-from torchrl.modules import NormalParamWrapper, TanhNormal, ProbabilisticActor, ValueOperator
+    CatTensors, DoubleToFloat, EnvCreator, CatFrames, StepCounter
+from torchrl.envs.libs.gym import GymEnv
+from torchrl.envs.utils import set_exploration_mode, check_env_specs
+from torchrl.modules import TanhNormal, ProbabilisticActor, ValueOperator
 from torchrl.objectives.value import GAE
+from torchrl.objectives import ClipPPOLoss
 from torchrl.objectives.utils import distance_loss
 from torchrl.data.replay_buffers.storages import LazyTensorStorage
 from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement
-from torchrl.data.replay_buffers.rb_prototype import ReplayBuffer
+from torchrl.data.replay_buffers import ReplayBuffer
 from tqdm import tqdm
 
 ######################################################################
@@ -90,147 +110,200 @@
 # We set the hyperparameters for our algorithm. Depending on the resources
 # available, one may choose to execute the policy on CUDA or on another
 # device.
-# The :obj:`frame_skip` will control how for how many frames is a single
+# The ``frame_skip`` will control how for how many frames is a single
 # action being executed. The rest of the arguments that count frames
 # must be corrected for this value (since one environment step will
-# actually return :obj:`frame_skip` frames).
+# actually return ``frame_skip`` frames).
 #
+
 num_procs = 4
-num_cells = 128
-frame_skip = 2
+num_cells = 256
+frame_skip = 1
 frames_per_batch = 2000 // frame_skip
-max_frames_per_traj = 1000 // frame_skip
-total_frames = 100_000 // frame_skip
+total_frames = 5_000 // frame_skip
 device = "cpu"
 gamma = 0.99
 lmbda = 0.95
 batch_size = 64  # total batch-size to compute the loss
 num_epochs = 10  # optimisation steps per batch of data collected
 clip_epsilon = 0.2  # clip value for PPO loss
-lr_policy = 3e-4
-lr_value = 1e-3
+lr = 3e-4
 max_grad_norm = 1.0
-n_entropy_samples = 10
 entropy_eps = 1e-4
 seed = 0
 
 ######################################################################
-# Define a parallel environment
-# -----------------------------
+# Define an environment
+# ---------------------
 #
-# A parallel environment is defined with a custom function that returns an environment
-# instance and a number of workers to spawn. The environment creator function should be
-# wrapped in an :obj:`EnvCreator` instance to keep track of the meta-data.
-# For this task, we'll focus on the cheetah-run task from the DeepMind control suite
-# (https://linkinghub.elsevier.com/retrieve/pii/S2665963820300099)
+# In RL, an 'environment' is usually the way we refer to a simulator or a
+# control system. Various libraries provide simulation environments for reinforcement
+# learning, including Gymnasium (previously OpenAI Gym), DeepMind control suite, and
+# many others.
+# As a generalistic library, TorchRL's goal is to provide an interchangeable interface
+# to a large panel of RL simulators, allowing you to easily swap one environment
+# with another. For example, creating a wrapped gym environment can be achieved with few characters:
 #
-torch.manual_seed(seed)
 
-def env_maker(from_pixels=False):
-    return DMControlEnv("cheetah", "run", device=device,
-                        frame_skip=frame_skip, from_pixels=from_pixels)
-
-
-parallel_env = ParallelEnv(num_procs, EnvCreator(env_maker))
-parallel_env.set_seed(seed)
+base_env = GymEnv("InvertedDoublePendulum-v4", device=device, frame_skip=frame_skip)
+torch.manual_seed(seed)
+base_env.set_seed(seed)
 
 ######################################################################
+# There are already a few things to notice in this code: first, we created
+# the environment by calling the ``GymEnv`` wrapper. If extra keyword arguments
+# are passed, they will be transmitted to the ``gym.make`` method, hence covering
+# the most common env construction commands.
+# Alternatively, one could also directly create a gym environment using ``gym.make(env_name, **kwargs)``
+# and wrap it in a `GymWrapper` class.
+#
+# Notice also the ``device`` argument: for gym, this only controls the device where
+# input action and observered states will be stored, but the execution will always
+# be done on CPU. The reason for this is simply that gym does not support on-device
+# execution, unless specified otherwise. For other libraries, we have control over
+# the execution device and, as much as we can, we try to stay consistent in terms of
+# storing and execution backends.
+#
 # Transforms
 # ----------
 #
 # We will append some transforms to our environments to prepare the data for
-# the policy. The :obj:`CatTensors` transform will concatenate the two observation
-# keys ("position" and "velocity") into a single state named "observation".
-# We will then normalize the data. In general, it is preferable to have data
-# that loosely match a unit Gaussian distribution: to obtain this, we will
+# the policy. In Gym, this is usually achieved via wrappers. TorchRL takes a different
+# approach, more similar to other pytorch domain libraries, through the use of transforms.
+# To add transforms to an environment, one should simply wrap it in a :class:`TransformedEnv`
+# instance, and append the sequence of transforms to it. The transformed env will inherit
+# the device and meta-data of the wrapped env, and transform these depending on the sequence
+# of transforms it contains.
+#
+# Normalization
+# -------------
+#
+# The first to encode is a normalization transform.
+# As a rule of thumbs, it is preferable to have data that loosely
+# match a unit Gaussian distribution: to obtain this, we will
 # run a certain number of random steps in the environment and compute
 # the summary statistics of these observations.
-# Since we're executing the environments in parallel, we can execute this
-# normalization in batch.
-# Finally, dm_control returns values in float64 and expects the actions to
-# be in the same format. Since our network is parameterized with single
-# precision numbers, we must transform the actions to float64 and then transform
-# the reward and the observation back to float32. The :obj:`DoubleToFloat`
-# has, as all TorchRL transforms do, a forward and an inverse method.
-# The inverse is being called before the environment step is executed, the
-# forward after. Hence, we just need to specify which entry has to be
-# transformed when the data (i.e. the action) gets in and which when the
-# data (i.e. the observation) is returned.
+#
+# We'll append two other transforms: the :class:`DoubleToFloat` transform will
+# convert double entries to single-precision numbers, ready to be read by the
+# policy. The :class:`StepCounter` transform will be used to count the steps before
+# the environment is terminated. We will use this measure as a supplementary measure
+# of performance.
 #
 
 env = TransformedEnv(
-    parallel_env,
+    base_env,
     Compose(
-        CatTensors(["position", "velocity"], "observation"),
-        # concatenates the observations in a single vector
+        # normalize observations
         ObservationNorm(in_keys=["observation"]),
-        # dm_control returns float64 observations and expects float64 actions
-        DoubleToFloat(in_keys=["observation", "reward"],
-                      in_keys_inv=["action"]),
+        DoubleToFloat(in_keys=["observation"], ),
+        StepCounter(),
     )
 )
 
 ######################################################################
-# :obj:`ObservationNorm` can automatically gather the summary statistics of
-# our environment:
+# As you may have noticed, we have created a normalization layer but we did not
+# set its normalization parameters. To do this, :class:`ObservationNorm` can
+# automatically gather the summary statistics of our environment:
 #
-env.transform[1].init_stats(max_frames_per_traj // frame_skip * env.batch_size[0], (0, 1), 1)
-# check the shape of our summary stats
-print("normalization constant shape:", env.transform[1].loc.shape)
+env.transform[0].init_stats(num_iter=1000, reduce_dim=0, cat_dim=0)
+
+# A little sanity check for the shape of our summary stats:
+print("normalization constant shape:", env.transform[0].loc.shape)
+
 
 ######################################################################
-# We create an evaluation environment to check the policy performance in
-# pure exploitation mode:
+# An environment is not only defined by its simulator and transforms, but also
+# by a series of metadata that describe what can be expected during its execution.
+# For efficiency purposes, TorchRL is quite stringent when it comes to environment specs,
+# but you can easily check that your environment specs are adequate.
+# Let's see a concrete example using our transformed environment by looking at its specs.
+# There are three specs to look at: ``observation_spec`` which defines what is to
+# be expected when executing an action in the environment, ``reward_spec`` which indicates
+# the reward domain and finally the ``input_spec`` (which contains the ``action_spec``)
+# and which represents everything an environment requires to execute a single step.
 #
-eval_env = TransformedEnv(
-    ParallelEnv(
-        num_procs,
-        EnvCreator(lambda: env_maker(from_pixels=False))
-    ),
-    env.transform.clone())
-assert (eval_env.transform[1].loc == env.transform[1].loc).all()
+print("observation_spec:", env.observation_spec)
+print("reward_spec:", env.reward_spec)
+print("input_spec:", env.input_spec)
+print("action_spec (as defined by input_spec):", env.action_spec)
 
 ######################################################################
+# the :func:`check_env_specs` function runs a small rollout and compares its output against the environemnt
+# specs. If no error is raised, we can be confident that the specs are properly defined:
+#
+check_env_specs(env)
+
+######################################################################
+# For fun, let's see what a simple random rollout looks like. You can
+# call `env.rollout(n_steps)` and get an overview of what the environment inputs
+# and outputs look like. Actions will automatically be drawn from the action spec
+# domain, so you don't need to care about designing a random sampler.
+#
+# Typically, at each step, an RL environment receives an
+# action as input, and outputs an observation, a reward and a done state. The
+# observation may be composite, meaning that it could be composed of more than one
+# tensor. This is not a problem for TorchRL, since the whole set of observations
+# is automatically packed in the output :class:`TensorDict`. After executing a rollout
+# (ie a sequence of environment steps and random action generations) over a given
+# number of steps, we will retrieve a :class:`TensorDict` instance with a shape
+# that matches this trajectory length:
+#
+rollout = env.rollout(3)
+print("rollout of three steps:", rollout)
+print("Shape of the rollout TensorDict:", rollout.batch_size)
+
+######################################################################
+# Our rollout data has a batch-size of ``torch.Size([3])`, which matches the number of steps
+# we ran it for. The ``"next"`` entry points to the data coming after the current step.
+# In most cases, the ``"next""`` data at time `t` matches the data at ``t+1``, but this
+# may not be the case if we are using some specific transformations (e.g. mutli-step).
+#
 # Policy
 # ------
 #
-# PPO utilizes a stochastic policy to handle exploration.
+# PPO utilizes a stochastic policy to handle exploration. This means that our
+# neural network will have to output the parameters of a distribution, rather than
+# a single value corresponding to the action taken.
+#
 # As the data is continuous, we use a Tanh-Normal distribution to respect the
-# distribution bounds.
+# action space boundaries. TorchRL provides such distribution, and the only thing we
+# need to care about is to build a neural network that outputs the right number of
+# parameters for the policy to work with.
+#
 # We design the policy in three steps:
-# 1. Define a neural network D_obs -> 2 D_action (loc and scale both have dimension D_action)
-# 2. Wrap this neural network in a NormalParamWrapper to extract a location and a scale
-# 3. Create a probabilistic TensorDictModule that can create this distribution and sample from it.
+# 1. Define a neural network D_obs -> 2 D_action (loc and scale both have dimension D_action);
+# 2. Append a :class:`NormalParamExtractor` to extract a location and a scale (ie splits the input in two equal parts
+#   and applies a positive transformation to the scale parameter);
+# 3. Create a probabilistic :class:`TensorDictModule` that can create this distribution and sample from it.
 #
 
 actor_net = nn.Sequential(
     nn.LazyLinear(num_cells),
     nn.Tanh(),
-    nn.LazyLinear(num_cells),
-    nn.Tanh(),
-    nn.LazyLinear(2 * env.action_spec.shape[-1]),
-)
-value_net = nn.Sequential(
     nn.LazyLinear(num_cells),
     nn.Tanh(),
     nn.LazyLinear(num_cells),
     nn.Tanh(),
-    nn.LazyLinear(1),
+    nn.LazyLinear(2 * env.action_spec.shape[-1]),
+    NormalParamExtractor(),
 )
 
 ######################################################################
-# The output of the network must be a location and a non-negative scale.
-# The NormalParamWrapper class does just that transformation:
+# To enable the policy to "talk" with the environment through the tensordict
+# data carrier, we wrap the ``nn.Module`` in a :class:`TensorDictModule`. This
+# class will simply ready the ``in_keys`` it is provided with and write the
+# outputs in-place at the registered ``out_keys``.
 #
-actor_net = NormalParamWrapper(actor_net)
 policy_module = TensorDictModule(actor_net, in_keys=["observation"],
                                  out_keys=["loc", "scale"])
 
 ######################################################################
 # We now need to build a distribution out of the location and scale of our normal
-# distribution. As the action space is bounded, we'll use a TanhNormal distribution
-# to limit the values of the samples withing the accepted range. Here's how to code
-# this in TorchRL:
+# distribution. To do so, we instruct the :class:`ProbabilisticActor` class to
+# build a :class:`TanhNormal` out of the location and scale parameters. We also
+# provide the minimum and maximum values of this distribution, which we gather from the
+# environment specs.
 #
 policy_module = ProbabilisticActor(
     module=policy_module,
@@ -240,38 +313,80 @@ def env_maker(from_pixels=False):
     distribution_kwargs={"min": env.action_spec.space.minimum, "max": env.action_spec.space.maximum},
     return_log_prob=True,  # we'll need the log-prob for the numerator of the importance weights
 )
+
+######################################################################
+# Value network
+# -------------
+#
+# The value network is a crucial component of the PPO algorithm, even though it
+# won't be used at inference time. This module will read the observations and
+# return an estimation of the discounted return for the following trajectory.
+# This allows us to amortize learning by relying on the some utility estimation
+# that is learnt on-the-fly during training. Our value network share the same
+# structure as the policy, but for simplicity we assign it its own set of
+# parameters.
+#
+value_net = nn.Sequential(
+    nn.LazyLinear(num_cells),
+    nn.Tanh(),
+    nn.LazyLinear(num_cells),
+    nn.Tanh(),
+    nn.LazyLinear(num_cells),
+    nn.Tanh(),
+    nn.LazyLinear(1),
+)
+
 value_module = ValueOperator(
     module=value_net,
     in_keys=["observation"],
 )
 
 ######################################################################
-# let's try our policy:
+# let's try our policy and value modules. As we said earlier, the usage of
+# :class:`TensorDictModule` makes it possible to directly read the output
+# of the environment to run these modules, as they know what information to read
+# and where to write it:
 #
 print("Running policy:", policy_module(env.reset()))
 print("Running value:", value_module(env.reset()))
 
 ######################################################################
 # Data collector
+# --------------
 #
-# TorchRL provides a set of DataCollector classes. Briefly, these classes
+# TorchRL provides a set of :class:`DataCollector` classes. Briefly, these classes
 # allow you to control how many frames to collect at each iteration,
 # when to reset the environment, on which device the policy should be executed etc.
-
-collector = MultiSyncDataCollector(
-    # we'll just run one ParallelEnvironment. Adding elements to the list would increase the number of envs run in parallel
-    [env,],
+# The simplest data collector is the :class:`SyncDataCollector`: it is an iterator
+# that you can use to get batches of data of a given length, and that will stop
+# once a total number of frames have been collected.
+# As for the policy and environment before, the data collector will return :class:`TensorDict`
+# instances which allow you to write dataloading pipelines that are 100% oblivious
+# to the actual specificities of the rollout content.
+#
+collector = SyncDataCollector(
+    env,
     policy_module,
     frames_per_batch=frames_per_batch,
-    max_frames_per_traj=max_frames_per_traj,
     total_frames=total_frames,
+    split_trajs=False,
 )
 collector.set_seed(seed)
 
 ######################################################################
 # Replay buffer
+# -------------
+#
+# Replay buffers are a common building piece of off-policy RL algorithms.
+# In on-policy contexts, a replay buffer is refilled every time a batch of
+# data is collected, and its data is repeatedly consumed for a certain number
+# of epochs.
+# TorchRL's replay buffers are built using a common container :class:`ReplayBuffer` which
+# takes as argument the components of the buffer: a storage, a writer, a sampler
+# and possibly some transforms. Only the storage (which indicates the replay buffer
+# capacity) is mandatory. We also specify a sampler without repetition to
+# avoid sampling multiple times the same item in one epoch.
 #
-# We store the data in a replay buffer after every collection
 
 replay_buffer = ReplayBuffer(
     storage=LazyTensorStorage(frames_per_batch),
@@ -282,118 +397,137 @@ def env_maker(from_pixels=False):
 # Loss function
 # -------------
 #
-# The PPO loss can be directly imported from torchrl for convenience using
-#
-# .. code-block:: python
+# The PPO loss can be directly imported from torchrl for convenience using the
+# :class:`ClipPPOLoss` class. This is the easiest way of utilising PPO:
+# it hides away the mathematical operations of PPO and the control flow that
+# goes with it.
 #
-#    from torchrl.objectives import PPOClipLoss
-#    loss_module = ClipPPOLoss(policy, critic, gamma=gamma)
-#
-# For a more explicit description, we unwrap this code in the training loop.
-# Simply put, the objective function of PPO consists in maximising the expected
-# value of an advantage function for the policy parameters given some proximity
-# constraints. Let us first build the advantage module. PPO usually uses the GAE
-# (Generalized Advantage Estimator) function (https://arxiv.org/abs/1506.02438).
-# TorchRL provides a vectorized implementation of this function.
-# The GAE module will update the input tensordict with new "advantage" and "value_target" keys.
-# The value_target is a gradient-free tensor that represents the empirical
+# PPO requires some "advantage estimation" to be computed. In short, an advantage
+# is a value that reflects an expectancy over the return value while dealing with
+# the bias / variance tradeoff.
+# To compute the advantage, one just needs to (1) build the advantage module, which
+# utilises our value operator, and (2) pass each batch of data through it before each
+# epoch.
+# The GAE module will update the input tensordict with new ``"advantage"`` and
+# ``"value_target"`` entries.
+# The ``"value_target"`` is a gradient-free tensor that represents the empirical
 # value that the value network should represent with the input observation.
+# Both of these will be used by :class:`ClipPPOLoss` to
+# return the policy and value losses.
+#
 
 advantage_module = GAE(gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True)
 
+loss_module = ClipPPOLoss(actor=policy_module,
+                          critic=value_module,
+                          advantage_key= "advantage",
+                          clip_epsilon = 0.1,
+                          entropy_bonus = True,
+                          entropy_coef = entropy_eps,
+                          # these keys match by default but we set this for completeness
+                          value_target_key=advantage_module.value_target_key,
+                          critic_coef = 1.0,
+                          gamma = 0.99,
+                          loss_critic_type = "smooth_l1",
+                          )
+
+optim = torch.optim.Adam(loss_module.parameters(), lr)
+scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, total_frames // frames_per_batch, 0.0)
+
 ######################################################################
 # Training loop
 # -------------
+# We now have all the pieces needed to code our training loop.
+# The steps are quite easy: collect data, compute advantage, loop over the collected
+# data to compute loss values, backpropagate, optimize and repeat.
 #
 
-optim_policy = torch.optim.Adam(policy_module.parameters(), lr_policy)
-optim_value = torch.optim.Adam(value_module.parameters(), lr_value)
-scheduler_policy = torch.optim.lr_scheduler.CosineAnnealingLR(optim_policy, total_frames // frames_per_batch, lr_policy/2)
-scheduler_value = torch.optim.lr_scheduler.CosineAnnealingLR(optim_value, total_frames // frames_per_batch, lr_value/2)
 
-logs = []
-logs_eval_rewards = []
-# log-ratio bounds: log(1-eps) and log(1+eps)
-log_clip_bounds = (math.log1p(-clip_epsilon), math.log1p(clip_epsilon))
+logs = defaultdict(list)
 pbar = tqdm(total=total_frames * frame_skip)
 eval_str = ""
 for i, data in enumerate(collector):
-    logs.append(data["reward"].sum(-2).mean().item())
-    pbar.update(data["mask"].sum().item() * frame_skip)
-    cum_reward_str = f"cumulative reward={logs[-1]: 4.4f} (init={logs[0]: 4.4f})"
-    lr_str = f"lr policy: {optim_policy.param_groups[0]['lr']: 4.4f}"
-    pbar.set_description(", ".join([
-        eval_str,
-        cum_reward_str,
-        lr_str
-    ]))
     for k in range(num_epochs):
         # we place the data in the replay buffer after removing the time dimension
         # the "mask" key represents the valid data in the batch: by indexing with "mask"
         # we make sure that we eliminate all the 0-padding values.
         advantage_module(data)
-        replay_buffer.extend(data[data["mask"]])
+        data_view = data.reshape(-1)
+        replay_buffer.extend(data_view)
         for j in range(frames_per_batch // batch_size):
             subdata, *_ = replay_buffer.sample(batch_size)
-            # loss (1): Objective
-            action = subdata.get("action")
-            advantage = subdata.get("advantage")
-            dist = policy_module.get_dist(subdata.clone(recurse=False))
-            log_prob = dist.log_prob(action)
-            prev_log_prob = subdata.get("sample_log_prob")
-            # we need to unsqueeze the log_weight for the last dim, as
-            # the advantage has shape [batch, 1] but the log-probability has
-            # just size [batch]
-            log_weight = (log_prob - prev_log_prob).unsqueeze(-1)
-
-            gain1 = log_weight.exp() * advantage
-
-            log_weight_clip = log_weight.clamp(*log_clip_bounds)
-            gain2 = log_weight_clip.exp() * advantage
-
-            gain = torch.stack([gain1, gain2], -1).min(dim=-1)[0]
-            loss_objective = -gain.mean()
-
-            # Entropy bonus: we add a small entropy bonus to favour exploration
-            entropy_bonus = entropy_eps * dist.log_prob(dist.rsample((n_entropy_samples,))).mean()
-
-            # Optim
-            loss = loss_objective + entropy_bonus
-            loss.backward()
-            torch.nn.utils.clip_grad_norm_(optim_policy.param_groups[0]["params"], max_grad_norm)
-            optim_policy.step()
-            optim_policy.zero_grad()
-
-            # loss (2): Value.
-            # A simpler implementation would be to use GAE(..., gradient_mode=True), which
-            # would compute a differentiable "value_error" key-value pair to be used for the
-            # value network training.
-            value_module(subdata)
-            value = subdata.get("state_value")
-            loss_value = distance_loss(
-                value,
-                subdata["value_target"],
-                loss_function="smooth_l1",
-            ).mean()
+            loss_vals = loss_module(subdata)
+            loss_value = loss_vals["loss_objective"] + loss_vals["loss_critic"] + loss_vals["loss_entropy"]
 
             # Optim
             loss_value.backward()
-            torch.nn.utils.clip_grad_norm_(optim_value.param_groups[0]["params"], max_grad_norm)
-            optim_value.step()
-            optim_value.zero_grad()
-
-
-    collector.update_policy_weights_()
-    scheduler_policy.step()
-    scheduler_value.step()
+            torch.nn.utils.clip_grad_norm_(loss_module.parameters(), max_grad_norm)
+            optim.step()
+            optim.zero_grad()
+
+    logs["reward"].append(data["reward"].mean().item())
+    pbar.update(data.numel() * frame_skip)
+    cum_reward_str = f"average reward={logs['reward'][-1]: 4.4f} (init={logs['reward'][0]: 4.4f})"
+    logs["step_count"].append(data['step_count'].max().item())
+    stepcount_str = f"step count (max): {logs['step_count'][-1]}"
+    logs["lr"].append(optim.param_groups[0]['lr'])
+    lr_str = f"lr policy: {logs['lr'][-1]: 4.4f}"
     if i % 10 == 0:
         with set_exploration_mode("mean"), torch.no_grad():
-            eval_rollout = eval_env.rollout(max_frames_per_traj//frame_skip, policy_module)
-            eval_reward = eval_rollout["reward"].sum(-2).mean().item()
-            logs_eval_rewards.append(eval_reward)
-            eval_str = f"eval cumulative reward: {logs_eval_rewards[-1]: 4.4f} (init: {logs_eval_rewards[0]: 4.4f})"
+            # execute a rollout with the trained policy
+            eval_rollout = env.rollout(1000, policy_module)
+            logs["eval reward"].append(eval_rollout["reward"].mean().item())
+            logs["eval reward (sum)"].append(eval_rollout["reward"].sum().item())
+            logs["eval step_count"].append(eval_rollout["step_count"].max().item())
+            eval_str = f"eval cumulative reward: {logs['eval reward (sum)'][-1]: 4.4f} (init: {logs['eval reward (sum)'][0]: 4.4f}), eval step-count: {logs['eval step_count'][-1]}"
+            del eval_rollout
+    pbar.set_description(", ".join([
+        eval_str,
+        cum_reward_str,
+        stepcount_str,
+        lr_str
+    ]))
+
+    collector.update_policy_weights_()
+    scheduler.step()
+
+######################################################################
+# Results
+# -------
+#
+# Before the 1M step cap is reached, the algorithm should have reached a max
+# step count of 1000 steps, which is the maximum number of steps before the
+# trajectory is truncated.
+#
+plt.figure(figsize=(10, 10))
+plt.subplot(2, 2, 1)
+plt.plot(logs["reward"])
+plt.legend("training rewards (average)")
+plt.subplot(2, 2, 2)
+plt.plot(logs["step_count"])
+plt.legend("Max step count (training)")
+plt.subplot(2, 2, 3)
+plt.plot(logs["eval reward (sum)"])
+plt.legend("Return (test)")
+plt.subplot(2, 2, 4)
+plt.plot(logs["eval step_count"])
+plt.legend("Max step count (test)")
 
-plt.plot(logs)
 plt.savefig("training.png")
+
 collector.shutdown()
 del collector
+
+######################################################################
+# Next steps
+# ----------
+#
+# This algorithm could be improved in several way: from an efficiency perspective,
+# we could run several simulations in parallel to speed up data collection.
+# Check :obj:`torchrl.envs.ParallelEnv` for further information.
+#
+# From a logging perspective, one could add a :class:`VideoRecorder` transform to
+# the environment after asking for rendering to get a visual rendering of the
+# inverted pendulum in action. Check :obj:`torchrl.record.VideoRecorder` to
+# know more.
+#

From 162e9f4e79a0d2340cbc3ad75f30f7a99d92b613 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 14:30:28 +0000
Subject: [PATCH 11/52] refining

---
 requirements.txt | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index bfee5437f85..7d0b5260808 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -51,9 +51,8 @@ scikit-image
 scipy
 pillow==9.3.0
 wget
-gym==0.25.1
+gym[mujoco]==0.25.1
 gym-super-mario-bros==7.4.0
-dm_control
 pyopengl
 gymnasium==0.27.0
 timm

From 8c79f8797951e7168ed5e37d77e28db4311731db Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 14:30:48 +0000
Subject: [PATCH 12/52] gym version

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 7d0b5260808..855260f6243 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -51,7 +51,7 @@ scikit-image
 scipy
 pillow==9.3.0
 wget
-gym[mujoco]==0.25.1
+gym[mujoco]==0.26.1
 gym-super-mario-bros==7.4.0
 pyopengl
 gymnasium==0.27.0

From 19e2fda2282a24ebedcdb79693033526b1e3344e Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 15:12:51 +0000
Subject: [PATCH 13/52] gym version

---
 intermediate_source/reinforcement_ppo.py | 8 ++++----
 requirements.txt                         | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 2589a488c1f..ff5fcf1cff6 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -120,8 +120,8 @@
 num_cells = 256
 frame_skip = 1
 frames_per_batch = 2000 // frame_skip
-total_frames = 5_000 // frame_skip
-device = "cpu"
+total_frames = 500_000 // frame_skip
+device = "cpu" if not torch.has_cuda else "cuda:0"
 gamma = 0.99
 lmbda = 0.95
 batch_size = 64  # total batch-size to compute the loss
@@ -421,8 +421,8 @@
 loss_module = ClipPPOLoss(actor=policy_module,
                           critic=value_module,
                           advantage_key= "advantage",
-                          clip_epsilon = 0.1,
-                          entropy_bonus = True,
+                          clip_epsilon = clip_epsilon,
+                          entropy_bonus = bool(entropy_eps),
                           entropy_coef = entropy_eps,
                           # these keys match by default but we set this for completeness
                           value_target_key=advantage_module.value_target_key,
diff --git a/requirements.txt b/requirements.txt
index 855260f6243..ebfcc0fc815 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -51,7 +51,7 @@ scikit-image
 scipy
 pillow==9.3.0
 wget
-gym[mujoco]==0.26.1
+gymnasium[mujoco]==0.26.1
 gym-super-mario-bros==7.4.0
 pyopengl
 gymnasium==0.27.0

From fafed966f88ff4a507675fd819e8ac38d27e4ead Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 15:16:09 +0000
Subject: [PATCH 14/52] gym version

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index ebfcc0fc815..20605994908 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,7 +25,7 @@ tensorboard
 jinja2==3.0.3
 pytorch-lightning
 torchx
-torchrl-nightly  # temporary
+torchrl
 ax-platform
 nbformat>=4.2.0
 datasets

From 5a5c84483948f3d52564aa8a028c303d32f76f45 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 15:20:50 +0000
Subject: [PATCH 15/52] gym version

---
 intermediate_source/reinforcement_ppo.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index ff5fcf1cff6..719970b6afc 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -279,13 +279,13 @@
 #
 
 actor_net = nn.Sequential(
-    nn.LazyLinear(num_cells),
+    nn.LazyLinear(num_cells, device=device),
     nn.Tanh(),
-    nn.LazyLinear(num_cells),
+    nn.LazyLinear(num_cells, device=device),
     nn.Tanh(),
-    nn.LazyLinear(num_cells),
+    nn.LazyLinear(num_cells, device=device),
     nn.Tanh(),
-    nn.LazyLinear(2 * env.action_spec.shape[-1]),
+    nn.LazyLinear(2 * env.action_spec.shape[-1], device=device),
     NormalParamExtractor(),
 )
 
@@ -327,13 +327,13 @@
 # parameters.
 #
 value_net = nn.Sequential(
-    nn.LazyLinear(num_cells),
+    nn.LazyLinear(num_cells, device=device),
     nn.Tanh(),
-    nn.LazyLinear(num_cells),
+    nn.LazyLinear(num_cells, device=device),
     nn.Tanh(),
-    nn.LazyLinear(num_cells),
+    nn.LazyLinear(num_cells, device=device),
     nn.Tanh(),
-    nn.LazyLinear(1),
+    nn.LazyLinear(1, device=device),
 )
 
 value_module = ValueOperator(

From 8238cfb457886da9c596b6fb8c81a95c04917568 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 15:44:27 +0000
Subject: [PATCH 16/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 124 ++++++++++++-----------
 1 file changed, 65 insertions(+), 59 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 719970b6afc..ccf02ed567a 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -5,8 +5,8 @@
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
 This tutorial shows how to use PyTorch and TorchRL to train a parametric policy
-network to solve the Ant task from the OpenAI-Gym/Farama-Gymnasium control library
-<https://github.com/Farama-Foundation/Gymnasium>`__.
+network to solve the Ant task from the
+<OpenAI-Gym/Farama-Gymnasium control library https://github.com/Farama-Foundation/Gymnasium>`_.
 
 Key learning items:
 - How to create an environment in TorchRL, transform its outputs, and collect data from this env;
@@ -21,66 +21,68 @@
 
 .. code-block:: bash
 
-   %%bash
    pip3 install torchrl
    pip3 install gym[mujoco]
    pip3 install tqdm
 
-Proximal Policy Optimization (PPO) is a policy-gradient algorithm (think of it
-as an elaborated version of REINFORCE) where a batch of data is being
-collected and directly consumed to train the policy to maximise
-the expected return given some proximality constraints.
-
-Ref: https://arxiv.org/abs/1707.06347
-
-PPO is usually regarded as a fast and efficient method for online, on-policy
-reinforcement algorithm. TorchRL provides a loss-module that does all the work
-for you, so that you can rely on this implementation and focus on solving your
-problem rather than re-inventing the wheel every time you want to train a policy.
-
-For completeness, here is a brief overview of what the loss computes, even though
-this is taken care of by our :class:`ClipPPOLoss` module:
-The algorithm works as follows: we will sample a batch of data by playing the
-policy in the environment for a given number of steps. Then, we will perform a
-given number of optimization steps with random sub-samples of this batch using
-a clipped version of the REINFORCE loss.
-The clipping will put a pessimistic bound on our loss: lower return estimates will
-be favoured compared to higher ones.
-The precise formula of the loss is:
-
-.. math::
-
-  L(s,a,\theta_k,\theta) = \min\left(
-\frac{\pi_{\theta}(a|s)}{\pi_{\theta_k}(a|s)}  A^{\pi_{\theta_k}}(s,a), \;\;
-g(\epsilon, A^{\pi_{\theta_k}}(s,a))
-\right),
-
-There are two components in that loss: in the first part of the minimum operator,
-we simply compute an importance-weighted version of the REINFORCE loss (i.e. a
-REINFORCE loss that we have corrected for the fact that the current policy
-configuration lags the one that was used for the data collection).
-The second part of that minimum operator is a similar loss where we have clipped
-the ratios when they exceeded or were below a given pair of thresholds.
-
-This loss ensures that whether the advantage is positive or negative, policy
-updates that would produce significant shifts from the previous configuration
-are being discouraged.
-
-This tutorial is structured as follows: first, we will define the set of hyperparameters
-we will be using for training.
-Next, we will focus on creating our environment, or simulator, using TorchRL's
-wrappers and transforms. We will design the policy network and the value model,
-which is indispensable to the loss function. These modules will be used to configure
-our loss module, and finally we will be creating the replay buffer and data loader.
-
-Once all these elements are on stage, we will be able to run our training loop.
-
-Throughout this tutorial, we'll be using the :py:mod:`tensordict` library. :class:`TensorDict`
-is the lingua franca of TorchRL: it helps us abstract what a module reads and writes
-and care less about the specific data description and more about the algorithm itself.
-
 """
 
+######################################################################
+#
+# Proximal Policy Optimization (PPO) is a policy-gradient algorithm (think of it
+# as an elaborated version of REINFORCE) where a batch of data is being
+# collected and directly consumed to train the policy to maximise
+# the expected return given some proximality constraints.
+#
+# Ref: https://arxiv.org/abs/1707.06347
+#
+# PPO is usually regarded as a fast and efficient method for online, on-policy
+# reinforcement algorithm. TorchRL provides a loss-module that does all the work
+# for you, so that you can rely on this implementation and focus on solving your
+# problem rather than re-inventing the wheel every time you want to train a policy.
+#
+# For completeness, here is a brief overview of what the loss computes, even though
+# this is taken care of by our :class:`ClipPPOLoss` module:
+# The algorithm works as follows: we will sample a batch of data by playing the
+# policy in the environment for a given number of steps. Then, we will perform a
+# given number of optimization steps with random sub-samples of this batch using
+# a clipped version of the REINFORCE loss.
+# The clipping will put a pessimistic bound on our loss: lower return estimates will
+# be favoured compared to higher ones.
+# The precise formula of the loss is:
+#
+# .. math::
+#
+#    L(s,a,\theta_k,\theta) = \min\left(
+# \frac{\pi_{\theta}(a|s)}{\pi_{\theta_k}(a|s)}  A^{\pi_{\theta_k}}(s,a), \;\;
+# g(\epsilon, A^{\pi_{\theta_k}}(s,a))
+# \right),
+#
+# There are two components in that loss: in the first part of the minimum operator,
+# we simply compute an importance-weighted version of the REINFORCE loss (i.e. a
+# REINFORCE loss that we have corrected for the fact that the current policy
+# configuration lags the one that was used for the data collection).
+# The second part of that minimum operator is a similar loss where we have clipped
+# the ratios when they exceeded or were below a given pair of thresholds.
+#
+# This loss ensures that whether the advantage is positive or negative, policy
+# updates that would produce significant shifts from the previous configuration
+# are being discouraged.
+#
+# This tutorial is structured as follows: first, we will define the set of hyperparameters
+# we will be using for training.
+# Next, we will focus on creating our environment, or simulator, using TorchRL's
+# wrappers and transforms. We will design the policy network and the value model,
+# which is indispensable to the loss function. These modules will be used to configure
+# our loss module, and finally we will be creating the replay buffer and data loader.
+#
+# Once all these elements are on stage, we will be able to run our training loop.
+#
+# Throughout this tutorial, we'll be using the :py:mod:`tensordict` library. :class:`TensorDict`
+# is the lingua franca of TorchRL: it helps us abstract what a module reads and writes
+# and care less about the specific data description and more about the algorithm itself.
+
+
 import math
 from collections import defaultdict
 
@@ -272,9 +274,12 @@
 # parameters for the policy to work with.
 #
 # We design the policy in three steps:
+#
 # 1. Define a neural network D_obs -> 2 D_action (loc and scale both have dimension D_action);
+#
 # 2. Append a :class:`NormalParamExtractor` to extract a location and a scale (ie splits the input in two equal parts
 #   and applies a positive transformation to the scale parameter);
+#
 # 3. Create a probabilistic :class:`TensorDictModule` that can create this distribution and sample from it.
 #
 
@@ -491,6 +496,10 @@
     collector.update_policy_weights_()
     scheduler.step()
 
+
+collector.shutdown()
+del collector
+
 ######################################################################
 # Results
 # -------
@@ -515,9 +524,6 @@
 
 plt.savefig("training.png")
 
-collector.shutdown()
-del collector
-
 ######################################################################
 # Next steps
 # ----------

From 5bc7f33a4aa6d917e15f64e8fc753ea000ad274d Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 15:53:19 +0000
Subject: [PATCH 17/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 109 ++++++++++++++---------
 1 file changed, 66 insertions(+), 43 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index ccf02ed567a..4feef7ab9a0 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -53,10 +53,10 @@
 #
 # .. math::
 #
-#    L(s,a,\theta_k,\theta) = \min\left(
-# \frac{\pi_{\theta}(a|s)}{\pi_{\theta_k}(a|s)}  A^{\pi_{\theta_k}}(s,a), \;\;
-# g(\epsilon, A^{\pi_{\theta_k}}(s,a))
-# \right),
+#     L(s,a,\theta_k,\theta) = \min\left(
+#     \frac{\pi_{\theta}(a|s)}{\pi_{\theta_k}(a|s)}  A^{\pi_{\theta_k}}(s,a), \;\;
+#     g(\epsilon, A^{\pi_{\theta_k}}(s,a))
+#     \right),
 #
 # There are two components in that loss: in the first part of the minimum operator,
 # we simply compute an importance-weighted version of the REINFORCE loss (i.e. a
@@ -83,7 +83,6 @@
 # and care less about the specific data description and more about the algorithm itself.
 
 
-import math
 from collections import defaultdict
 
 import matplotlib.pyplot as plt
@@ -92,17 +91,16 @@
 from tensordict.nn.distributions import NormalParamExtractor
 from torch import nn
 from torchrl.collectors import SyncDataCollector
-from torchrl.envs import ParallelEnv, TransformedEnv, ObservationNorm, Compose, \
-    CatTensors, DoubleToFloat, EnvCreator, CatFrames, StepCounter
+from torchrl.data.replay_buffers import ReplayBuffer
+from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement
+from torchrl.data.replay_buffers.storages import LazyTensorStorage
+from torchrl.envs import TransformedEnv, ObservationNorm, Compose, \
+    DoubleToFloat, StepCounter
 from torchrl.envs.libs.gym import GymEnv
 from torchrl.envs.utils import set_exploration_mode, check_env_specs
 from torchrl.modules import TanhNormal, ProbabilisticActor, ValueOperator
-from torchrl.objectives.value import GAE
 from torchrl.objectives import ClipPPOLoss
-from torchrl.objectives.utils import distance_loss
-from torchrl.data.replay_buffers.storages import LazyTensorStorage
-from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement
-from torchrl.data.replay_buffers import ReplayBuffer
+from torchrl.objectives.value import GAE
 from tqdm import tqdm
 
 ######################################################################
@@ -147,7 +145,10 @@
 # with another. For example, creating a wrapped gym environment can be achieved with few characters:
 #
 
-base_env = GymEnv("InvertedDoublePendulum-v4", device=device, frame_skip=frame_skip)
+base_env = GymEnv(
+    "InvertedDoublePendulum-v4", device=device,
+    frame_skip=frame_skip
+    )
 torch.manual_seed(seed)
 base_env.set_seed(seed)
 
@@ -213,7 +214,6 @@
 # A little sanity check for the shape of our summary stats:
 print("normalization constant shape:", env.transform[0].loc.shape)
 
-
 ######################################################################
 # An environment is not only defined by its simulator and transforms, but also
 # by a series of metadata that describe what can be expected during its execution.
@@ -300,8 +300,10 @@
 # class will simply ready the ``in_keys`` it is provided with and write the
 # outputs in-place at the registered ``out_keys``.
 #
-policy_module = TensorDictModule(actor_net, in_keys=["observation"],
-                                 out_keys=["loc", "scale"])
+policy_module = TensorDictModule(
+    actor_net, in_keys=["observation"],
+    out_keys=["loc", "scale"]
+    )
 
 ######################################################################
 # We now need to build a distribution out of the location and scale of our normal
@@ -315,8 +317,10 @@
     spec=env.action_spec,
     in_keys=["loc", "scale"],
     distribution_class=TanhNormal,
-    distribution_kwargs={"min": env.action_spec.space.minimum, "max": env.action_spec.space.maximum},
-    return_log_prob=True,  # we'll need the log-prob for the numerator of the importance weights
+    distribution_kwargs={"min": env.action_spec.space.minimum,
+                         "max": env.action_spec.space.maximum},
+    return_log_prob=True,
+    # we'll need the log-prob for the numerator of the importance weights
 )
 
 ######################################################################
@@ -421,23 +425,31 @@
 # return the policy and value losses.
 #
 
-advantage_module = GAE(gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True)
+advantage_module = GAE(
+    gamma=gamma, lmbda=lmbda, value_network=value_module,
+    average_gae=True
+)
 
-loss_module = ClipPPOLoss(actor=policy_module,
-                          critic=value_module,
-                          advantage_key= "advantage",
-                          clip_epsilon = clip_epsilon,
-                          entropy_bonus = bool(entropy_eps),
-                          entropy_coef = entropy_eps,
-                          # these keys match by default but we set this for completeness
-                          value_target_key=advantage_module.value_target_key,
-                          critic_coef = 1.0,
-                          gamma = 0.99,
-                          loss_critic_type = "smooth_l1",
-                          )
+loss_module = ClipPPOLoss(
+    actor=policy_module,
+    critic=value_module,
+    advantage_key="advantage",
+    clip_epsilon=clip_epsilon,
+    entropy_bonus=bool(entropy_eps),
+    entropy_coef=entropy_eps,
+    # these keys match by default but we set this for completeness
+    value_target_key=advantage_module.value_target_key,
+    critic_coef=1.0,
+    gamma=0.99,
+    loss_critic_type="smooth_l1",
+)
 
 optim = torch.optim.Adam(loss_module.parameters(), lr)
-scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, total_frames // frames_per_batch, 0.0)
+scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+    optim,
+    total_frames // frames_per_batch,
+    0.0
+    )
 
 ######################################################################
 # Training loop
@@ -462,11 +474,15 @@
         for j in range(frames_per_batch // batch_size):
             subdata, *_ = replay_buffer.sample(batch_size)
             loss_vals = loss_module(subdata)
-            loss_value = loss_vals["loss_objective"] + loss_vals["loss_critic"] + loss_vals["loss_entropy"]
+            loss_value = loss_vals["loss_objective"] + loss_vals[
+                "loss_critic"] + loss_vals["loss_entropy"]
 
             # Optim
             loss_value.backward()
-            torch.nn.utils.clip_grad_norm_(loss_module.parameters(), max_grad_norm)
+            torch.nn.utils.clip_grad_norm_(
+                loss_module.parameters(),
+                max_grad_norm
+                )
             optim.step()
             optim.zero_grad()
 
@@ -482,21 +498,28 @@
             # execute a rollout with the trained policy
             eval_rollout = env.rollout(1000, policy_module)
             logs["eval reward"].append(eval_rollout["reward"].mean().item())
-            logs["eval reward (sum)"].append(eval_rollout["reward"].sum().item())
-            logs["eval step_count"].append(eval_rollout["step_count"].max().item())
+            logs["eval reward (sum)"].append(
+                eval_rollout["reward"].sum().item()
+            )
+            logs["eval step_count"].append(
+                eval_rollout["step_count"].max().item()
+            )
             eval_str = f"eval cumulative reward: {logs['eval reward (sum)'][-1]: 4.4f} (init: {logs['eval reward (sum)'][0]: 4.4f}), eval step-count: {logs['eval step_count'][-1]}"
             del eval_rollout
-    pbar.set_description(", ".join([
-        eval_str,
-        cum_reward_str,
-        stepcount_str,
-        lr_str
-    ]))
+    pbar.set_description(
+        ", ".join(
+            [
+                eval_str,
+                cum_reward_str,
+                stepcount_str,
+                lr_str
+            ]
+        )
+    )
 
     collector.update_policy_weights_()
     scheduler.step()
 
-
 collector.shutdown()
 del collector
 

From f8e14436923304a50aeb1b70e499cb511f4a6a51 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 21:02:58 +0000
Subject: [PATCH 18/52] minor

---
 intermediate_source/reinforcement_ppo.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 4feef7ab9a0..48d981f7c2e 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -101,6 +101,7 @@
 from torchrl.modules import TanhNormal, ProbabilisticActor, ValueOperator
 from torchrl.objectives import ClipPPOLoss
 from torchrl.objectives.value import GAE
+from torchrl.record import VideoRecorder
 from tqdm import tqdm
 
 ######################################################################
@@ -465,9 +466,6 @@
 eval_str = ""
 for i, data in enumerate(collector):
     for k in range(num_epochs):
-        # we place the data in the replay buffer after removing the time dimension
-        # the "mask" key represents the valid data in the batch: by indexing with "mask"
-        # we make sure that we eliminate all the 0-padding values.
         advantage_module(data)
         data_view = data.reshape(-1)
         replay_buffer.extend(data_view)
@@ -544,8 +542,7 @@
 plt.subplot(2, 2, 4)
 plt.plot(logs["eval step_count"])
 plt.legend("Max step count (test)")
-
-plt.savefig("training.png")
+plt.show()
 
 ######################################################################
 # Next steps
@@ -560,3 +557,4 @@
 # inverted pendulum in action. Check :obj:`torchrl.record.VideoRecorder` to
 # know more.
 #
+

From e0eb5ee9d5efa0711f50ca2bf0e218f6269dfd9c Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 21:06:16 +0000
Subject: [PATCH 19/52] titles

---
 intermediate_source/reinforcement_ppo.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 48d981f7c2e..f529005d6f5 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -5,8 +5,8 @@
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
 This tutorial shows how to use PyTorch and TorchRL to train a parametric policy
-network to solve the Ant task from the
-<OpenAI-Gym/Farama-Gymnasium control library https://github.com/Farama-Foundation/Gymnasium>`_.
+network to solve the Ant task from the OpenAI-Gym/Farama-Gymnasium control library
+https://github.com/Farama-Foundation/Gymnasium.
 
 Key learning items:
 - How to create an environment in TorchRL, transform its outputs, and collect data from this env;
@@ -532,16 +532,16 @@
 plt.figure(figsize=(10, 10))
 plt.subplot(2, 2, 1)
 plt.plot(logs["reward"])
-plt.legend("training rewards (average)")
+plt.title("training rewards (average)")
 plt.subplot(2, 2, 2)
 plt.plot(logs["step_count"])
-plt.legend("Max step count (training)")
+plt.title("Max step count (training)")
 plt.subplot(2, 2, 3)
 plt.plot(logs["eval reward (sum)"])
-plt.legend("Return (test)")
+plt.title("Return (test)")
 plt.subplot(2, 2, 4)
 plt.plot(logs["eval step_count"])
-plt.legend("Max step count (test)")
+plt.title("Max step count (test)")
 plt.show()
 
 ######################################################################

From 41c35b8f36b72b2198ac2887ff9a99642ed165d1 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 21:32:04 +0000
Subject: [PATCH 20/52] amend

---
 _static/img/invpendulum.gif              | Bin 0 -> 30332 bytes
 intermediate_source/reinforcement_ppo.py |   5 +++++
 2 files changed, 5 insertions(+)
 create mode 100644 _static/img/invpendulum.gif

diff --git a/_static/img/invpendulum.gif b/_static/img/invpendulum.gif
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diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index f529005d6f5..bc6176408c4 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -8,6 +8,11 @@
 network to solve the Ant task from the OpenAI-Gym/Farama-Gymnasium control library
 https://github.com/Farama-Foundation/Gymnasium.
 
+.. figure:: /_static/img/invpendulum.gif
+   :alt: Inverted pendulum
+
+   Inverted pendulum
+
 Key learning items:
 - How to create an environment in TorchRL, transform its outputs, and collect data from this env;
 - How to make your classes talk to each other using :class:`TensorDict`;

From 9e664fcff0d5f848babd8a0fe2afb99fddfab7c9 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 21:38:27 +0000
Subject: [PATCH 21/52] amend

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index bc6176408c4..454fa26b223 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -126,7 +126,7 @@
 num_cells = 256
 frame_skip = 1
 frames_per_batch = 2000 // frame_skip
-total_frames = 500_000 // frame_skip
+total_frames = 50_000 // frame_skip
 device = "cpu" if not torch.has_cuda else "cuda:0"
 gamma = 0.99
 lmbda = 0.95

From 51be13bf36c566bd083bab2dac481bf1b76e1d31 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 21:38:43 +0000
Subject: [PATCH 22/52] amend

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 454fa26b223..0af08ceb9af 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -125,7 +125,7 @@
 num_procs = 4
 num_cells = 256
 frame_skip = 1
-frames_per_batch = 2000 // frame_skip
+frames_per_batch = 1000 // frame_skip
 total_frames = 50_000 // frame_skip
 device = "cpu" if not torch.has_cuda else "cuda:0"
 gamma = 0.99

From 9fd686f0ffa682df7af3fba5f37efa8b2f2faa55 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Tue, 7 Feb 2023 21:39:10 +0000
Subject: [PATCH 23/52] amend

---
 intermediate_source/reinforcement_ppo.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 0af08ceb9af..a62fede6936 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -126,6 +126,7 @@
 num_cells = 256
 frame_skip = 1
 frames_per_batch = 1000 // frame_skip
+# For a complete training, bring the number of frames up to 1M
 total_frames = 50_000 // frame_skip
 device = "cpu" if not torch.has_cuda else "cuda:0"
 gamma = 0.99

From 1cc55f4cc5a3e74e79de162b00f4f1a98ed2bc6d Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Wed, 8 Feb 2023 14:38:11 +0000
Subject: [PATCH 24/52] amend

---
 intermediate_source/reinforcement_ppo.py | 255 ++++++++++++++++-------
 1 file changed, 182 insertions(+), 73 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index a62fede6936..d34673df265 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -74,19 +74,26 @@
 # updates that would produce significant shifts from the previous configuration
 # are being discouraged.
 #
-# This tutorial is structured as follows: first, we will define the set of hyperparameters
-# we will be using for training.
-# Next, we will focus on creating our environment, or simulator, using TorchRL's
-# wrappers and transforms. We will design the policy network and the value model,
-# which is indispensable to the loss function. These modules will be used to configure
-# our loss module, and finally we will be creating the replay buffer and data loader.
+# This tutorial is structured as follows:
 #
-# Once all these elements are on stage, we will be able to run our training loop.
+# 1. First, we will define a set of hyperparameters we will be using for training.
+#
+# 2. Next, we will focus on creating our environment, or simulator, using TorchRL's
+#    wrappers and transforms.
+#
+# 3. Next, We will design the policy network and the value model,
+#    which is indispensable to the loss function. These modules will be used
+#    to configure our loss module.
+#
+# 4. Next, we will create the replay buffer and data loader.
+#
+# 5. Finally, we will run our training loop and analyze the results.
+#
+# Throughout this tutorial, we'll be using the :py:mod:`tensordict` library.
+# :class:`TensorDict` is the lingua franca of TorchRL: it helps us abstract
+# what a module reads and writes and care less about the specific data
+# description and more about the algorithm itself.
 #
-# Throughout this tutorial, we'll be using the :py:mod:`tensordict` library. :class:`TensorDict`
-# is the lingua franca of TorchRL: it helps us abstract what a module reads and writes
-# and care less about the specific data description and more about the algorithm itself.
-
 
 from collections import defaultdict
 
@@ -106,7 +113,6 @@
 from torchrl.modules import TanhNormal, ProbabilisticActor, ValueOperator
 from torchrl.objectives import ClipPPOLoss
 from torchrl.objectives.value import GAE
-from torchrl.record import VideoRecorder
 from tqdm import tqdm
 
 ######################################################################
@@ -124,21 +130,51 @@
 
 num_procs = 4
 num_cells = 256
-frame_skip = 1
-frames_per_batch = 1000 // frame_skip
-# For a complete training, bring the number of frames up to 1M
-total_frames = 50_000 // frame_skip
 device = "cpu" if not torch.has_cuda else "cuda:0"
 gamma = 0.99
 lmbda = 0.95
-batch_size = 64  # total batch-size to compute the loss
-num_epochs = 10  # optimisation steps per batch of data collected
-clip_epsilon = 0.2  # clip value for PPO loss
 lr = 3e-4
 max_grad_norm = 1.0
 entropy_eps = 1e-4
 seed = 0
 
+######################################################################
+# Data collection parameters
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# When collecting data, we will be able to choose how big each batch will be
+# by defining a ``frames_per_batch`` parameter. We will also define how many
+# frames (ie number of interactions with the simulator) we allow ourselves to
+# use. In general, the goal of an RL algorithm is to learn to solve the task
+# as fast as we can in terms of environment interactions: the lowere the ``total_frames``
+# the better.
+# We also define a ``frame_skip``: in some contexts, repeating the same action
+# multiple times over the course of a trajectory may be beneficial as it makes
+# the behaviour more consistent and less erratic. However, a "skipping"
+# too many frames will hamper training by reducing the reactivity of the actor
+# to observation changes.
+#
+frame_skip = 1
+frames_per_batch = 1000 // frame_skip
+# For a complete training, bring the number of frames up to 1M
+total_frames = 50_000 // frame_skip
+
+######################################################################
+# PPO parameters
+# ~~~~~~~~~~~~~~
+#
+# At each data collection (or batch collection) we will run the optimization
+# over a certain number of "epochs", each time consuming the entire data we just
+# acquired in a nested training loop. Here, the ``sub_batch_size`` is different from the
+# ``frames_per_batch`` here above: recall that we are working with a "batch of data"
+# coming from our collector, which size is defined by ``frames_per_batch``, and that
+# we will further split in smaller sub-batches during the inner training loop.
+# The size of these sub-batches is controlled by ``sub_batch_size``.
+#
+sub_batch_size = 64  # total batch-size to compute the loss
+num_epochs = 10  # optimisation steps per batch of data collected
+clip_epsilon = 0.2  # clip value for PPO loss: see the equation in the intro for more context.
+
 ######################################################################
 # Define an environment
 # ---------------------
@@ -155,7 +191,7 @@
 base_env = GymEnv(
     "InvertedDoublePendulum-v4", device=device,
     frame_skip=frame_skip
-    )
+)
 torch.manual_seed(seed)
 base_env.set_seed(seed)
 
@@ -200,6 +236,16 @@
 # the environment is terminated. We will use this measure as a supplementary measure
 # of performance.
 #
+# As we will see later, many of the TorchRL's classes rely on :class:`TensorDict`
+# to communicate. You could think of it as a python dictionary with some extra
+# tensor features. In practice, this means that many modules we will be working
+# with need to be told what key to read (``in_keys``) and what key to write
+# (``out_keys``) in the tensordict they will receive. Usually, if ``out_keys``
+# is omitted, it is assumed that the ``in_keys`` entries will be updated
+# in-place. For our transforms, the only entry we are interested in is referred
+# to as ``"observation"`` and our transform layers will be told to modify this
+# entry and this entry only:
+#
 
 env = TransformedEnv(
     base_env,
@@ -218,19 +264,32 @@
 #
 env.transform[0].init_stats(num_iter=1000, reduce_dim=0, cat_dim=0)
 
-# A little sanity check for the shape of our summary stats:
+######################################################################
+# The :class:`ObservationNorm` transform has now been populated with a
+# location and a scale that will be used to normalize the data.
+#
+# Let us do a little sanity check for the shape of our summary stats:
+#
 print("normalization constant shape:", env.transform[0].loc.shape)
 
 ######################################################################
 # An environment is not only defined by its simulator and transforms, but also
-# by a series of metadata that describe what can be expected during its execution.
-# For efficiency purposes, TorchRL is quite stringent when it comes to environment specs,
-# but you can easily check that your environment specs are adequate.
-# Let's see a concrete example using our transformed environment by looking at its specs.
-# There are three specs to look at: ``observation_spec`` which defines what is to
-# be expected when executing an action in the environment, ``reward_spec`` which indicates
-# the reward domain and finally the ``input_spec`` (which contains the ``action_spec``)
-# and which represents everything an environment requires to execute a single step.
+# by a series of metadata that describe what can be expected during its
+# execution.
+# For efficiency purposes, TorchRL is quite stringent when it comes to
+# environment specs, but you can easily check that your environment specs are
+# adequate.
+# In our example, the :class:`GymWrapper` and :class:`GymEnv` that inherits
+# from it already take care of setting the proper specs for your env so
+# you should not have to care about this.
+#
+# Nevertheless, let's see a concrete example using our transformed
+# environment by looking at its specs.
+# There are three specs to look at: ``observation_spec`` which defines what
+# is to be expected when executing an action in the environment,
+# ``reward_spec`` which indicates the reward domain and finally the
+# ``input_spec`` (which contains the ``action_spec``) and which represents
+# everything an environment requires to execute a single step.
 #
 print("observation_spec:", env.observation_spec)
 print("reward_spec:", env.reward_spec)
@@ -260,10 +319,10 @@
 #
 rollout = env.rollout(3)
 print("rollout of three steps:", rollout)
-print("Shape of the rollout TensorDict:", rollout.batch_size)
+print("Shape of the rollout TensorDict:", rollout.sub_batch_size)
 
 ######################################################################
-# Our rollout data has a batch-size of ``torch.Size([3])`, which matches the number of steps
+# Our rollout data has a shape of ``torch.Size([3])`, which matches the number of steps
 # we ran it for. The ``"next"`` entry points to the data coming after the current step.
 # In most cases, the ``"next""`` data at time `t` matches the data at ``t+1``, but this
 # may not be the case if we are using some specific transformations (e.g. mutli-step).
@@ -272,17 +331,25 @@
 # ------
 #
 # PPO utilizes a stochastic policy to handle exploration. This means that our
-# neural network will have to output the parameters of a distribution, rather than
-# a single value corresponding to the action taken.
+# neural network will have to output the parameters of a distribution, rather
+# than a single value corresponding to the action taken.
 #
 # As the data is continuous, we use a Tanh-Normal distribution to respect the
-# action space boundaries. TorchRL provides such distribution, and the only thing we
-# need to care about is to build a neural network that outputs the right number of
-# parameters for the policy to work with.
+# action space boundaries. TorchRL provides such distribution, and the only
+# thing we need to care about is to build a neural network that outputs the
+# right number of parameters for the policy to work with (a location, or mean,
+# and a scale):
+#
+# .. math::
+#
+#     f_{\theta}(\text{observation}) = \mu_{\theta}(\text{observation}), \sigma^{+}_{\theta}(\text{observation})
+#
+# The only extra-difficulty that is brought up here is to split our output in two
+# equal parts and map the second to a scrictly positive space.
 #
 # We design the policy in three steps:
 #
-# 1. Define a neural network D_obs -> 2 D_action (loc and scale both have dimension D_action);
+# 1. Define a neural network ``D_obs`` -> ``2 * D_action``. Indeed, our ``loc`` (mu) and ``scale`` (sigma) both have dimension ``D_action``;
 #
 # 2. Append a :class:`NormalParamExtractor` to extract a location and a scale (ie splits the input in two equal parts
 #   and applies a positive transformation to the scale parameter);
@@ -310,14 +377,22 @@
 policy_module = TensorDictModule(
     actor_net, in_keys=["observation"],
     out_keys=["loc", "scale"]
-    )
+)
 
 ######################################################################
-# We now need to build a distribution out of the location and scale of our normal
-# distribution. To do so, we instruct the :class:`ProbabilisticActor` class to
-# build a :class:`TanhNormal` out of the location and scale parameters. We also
-# provide the minimum and maximum values of this distribution, which we gather from the
-# environment specs.
+# We now need to build a distribution out of the location and scale of our
+# normal distribution. To do so, we instruct the :class:`ProbabilisticActor`
+# class to build a :class:`TanhNormal` out of the location and scale
+# parameters. We also provide the minimum and maximum values of this
+# distribution, which we gather from the environment specs.
+#
+# The name of the ``in_keys`` (and hence the name of the ``out_keys`` from
+# the :class:`TensorDictModule` above) cannot be set to any value one may
+# like, as the :class:`TanhNormal` distribution constructor will expect the
+# ``loc`` and ``scale`` keyword arguments. That being said,
+# :class:`ProbabilisticActor` also accepts ``Dict[str, str]`` typed ``in_keys``
+# where the key-value pair indicates what ``in_key`` string should be used for
+# every keyword argument that is to be used.
 #
 policy_module = ProbabilisticActor(
     module=policy_module,
@@ -370,15 +445,31 @@
 # Data collector
 # --------------
 #
-# TorchRL provides a set of :class:`DataCollector` classes. Briefly, these classes
-# allow you to control how many frames to collect at each iteration,
-# when to reset the environment, on which device the policy should be executed etc.
-# The simplest data collector is the :class:`SyncDataCollector`: it is an iterator
-# that you can use to get batches of data of a given length, and that will stop
-# once a total number of frames have been collected.
-# As for the policy and environment before, the data collector will return :class:`TensorDict`
-# instances which allow you to write dataloading pipelines that are 100% oblivious
-# to the actual specificities of the rollout content.
+# TorchRL provides a set of :class:`DataCollector` classes. Briefly, these
+# classes execute three operations: reset an environment, compute an action
+# given the latest observation, execute a step in the environment, and repeat
+# the last two steps until the environment reaches a stop signal (or ``"done"``
+# state).
+#
+# They allow you to control how many frames to collect at each iteration
+# (through the ``frames_per_batch`` parameter),
+# when to reset the environment (through the ``max_frames_per_traj`` argument),
+# on which ``device`` the policy should be executed, etc. They are also
+# designed to work efficiently with batched and multiprocessed environments.
+#
+# The simplest data collector is the :class:`SyncDataCollector`: it is an
+# iterator that you can use to get batches of data of a given length, and
+# that will stop once a total number of frames (``total_frames``) have been
+# collected.
+# Other data collectors (``MultiSyncDataCollector`` and
+# ``MultiaSyncDataCollector``) will execute the same operations in synchronous
+# and asynchronous manner over a set of multiprocessed workers.
+#
+# As for the policy and environment before, the data collector will return
+# :class:`TensorDict` instances with a total number of elements that will
+# match ``frames_per_batch``. Using :class:`TensorDict` to pass data to the
+# training loop allows you to write dataloading pipelines
+# that are 100% oblivious to the actual specificities of the rollout content.
 #
 collector = SyncDataCollector(
     env,
@@ -386,6 +477,7 @@
     frames_per_batch=frames_per_batch,
     total_frames=total_frames,
     split_trajs=False,
+    device=device,
 )
 collector.set_seed(seed)
 
@@ -397,11 +489,16 @@
 # In on-policy contexts, a replay buffer is refilled every time a batch of
 # data is collected, and its data is repeatedly consumed for a certain number
 # of epochs.
-# TorchRL's replay buffers are built using a common container :class:`ReplayBuffer` which
-# takes as argument the components of the buffer: a storage, a writer, a sampler
-# and possibly some transforms. Only the storage (which indicates the replay buffer
-# capacity) is mandatory. We also specify a sampler without repetition to
-# avoid sampling multiple times the same item in one epoch.
+#
+# TorchRL's replay buffers are built using a common container
+# :class:`ReplayBuffer` which takes as argument the components of the buffer:
+# a storage, a writer, a sampler and possibly some transforms. Only the
+# storage (which indicates the replay buffer capacity) is mandatory. We
+# also specify a sampler without repetition to avoid sampling multiple times
+# the same item in one epoch.
+# Using a replay buffer for PPO is not mandatory and we could simply
+# sample the sub-batches from the collected batch, but using these classes
+# make it easy for us to build the inner training loop in a reproducible way.
 #
 
 replay_buffer = ReplayBuffer(
@@ -456,7 +553,7 @@
     optim,
     total_frames // frames_per_batch,
     0.0
-    )
+)
 
 ######################################################################
 # Training loop
@@ -470,34 +567,49 @@
 logs = defaultdict(list)
 pbar = tqdm(total=total_frames * frame_skip)
 eval_str = ""
-for i, data in enumerate(collector):
+
+# We iterate over the collector until it reaches the total number of frames it was
+# designed to collect:
+for i, tensordict_data in enumerate(collector):
+    # we now have a batch of data to work with. Let's learn something from it.
     for k in range(num_epochs):
-        advantage_module(data)
-        data_view = data.reshape(-1)
+        # We'll need an "advantage" signal to make PPO work.
+        # We re-compute it at each epoch as its value depends on the value
+        # network which is updated in the inner loop.
+        advantage_module(tensordict_data)
+        data_view = tensordict_data.reshape(-1)
         replay_buffer.extend(data_view)
-        for j in range(frames_per_batch // batch_size):
-            subdata, *_ = replay_buffer.sample(batch_size)
+        for j in range(frames_per_batch // sub_batch_size):
+            subdata, *_ = replay_buffer.sample(sub_batch_size)
             loss_vals = loss_module(subdata)
             loss_value = loss_vals["loss_objective"] + loss_vals[
                 "loss_critic"] + loss_vals["loss_entropy"]
 
-            # Optim
+            # Optimization: backward, grad clipping and optim step
             loss_value.backward()
+            # this is not strictly mandatory but it's good practice to keep
+            # your gradient norm bounded
             torch.nn.utils.clip_grad_norm_(
                 loss_module.parameters(),
                 max_grad_norm
-                )
+            )
             optim.step()
             optim.zero_grad()
 
-    logs["reward"].append(data["reward"].mean().item())
-    pbar.update(data.numel() * frame_skip)
+    logs["reward"].append(tensordict_data["reward"].mean().item())
+    pbar.update(tensordict_data.numel() * frame_skip)
     cum_reward_str = f"average reward={logs['reward'][-1]: 4.4f} (init={logs['reward'][0]: 4.4f})"
-    logs["step_count"].append(data['step_count'].max().item())
+    logs["step_count"].append(tensordict_data['step_count'].max().item())
     stepcount_str = f"step count (max): {logs['step_count'][-1]}"
     logs["lr"].append(optim.param_groups[0]['lr'])
     lr_str = f"lr policy: {logs['lr'][-1]: 4.4f}"
     if i % 10 == 0:
+        # We evaluate the policy once every 10 batches of data.
+        # Evaluation is rather simple: execute the policy without exploration
+        # (take the expected value of the action distribution) for a given
+        # number of steps (1000, which is our env horizon).
+        # The ``rollout`` method of the env can take a policy as argument:
+        # it will then execute this policy at each step.
         with set_exploration_mode("mean"), torch.no_grad():
             # execute a rollout with the trained policy
             eval_rollout = env.rollout(1000, policy_module)
@@ -521,12 +633,10 @@
         )
     )
 
-    collector.update_policy_weights_()
+    # We're also using a learning rate scheduler. Like the gradient clipping,
+    # this is a nice-to-have but nothing necessary for PPO to work.
     scheduler.step()
 
-collector.shutdown()
-del collector
-
 ######################################################################
 # Results
 # -------
@@ -563,4 +673,3 @@
 # inverted pendulum in action. Check :obj:`torchrl.record.VideoRecorder` to
 # know more.
 #
-

From d1468f2aee081f3aa4ad3a233b7496e202ec8b81 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Wed, 8 Feb 2023 14:59:52 +0000
Subject: [PATCH 25/52] amend

---
 intermediate_source/reinforcement_ppo.py | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index d34673df265..b1e1fdcbb01 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -128,15 +128,10 @@
 # actually return ``frame_skip`` frames).
 #
 
-num_procs = 4
-num_cells = 256
 device = "cpu" if not torch.has_cuda else "cuda:0"
-gamma = 0.99
-lmbda = 0.95
+num_cells = 256  # number of cells in each layer
 lr = 3e-4
 max_grad_norm = 1.0
-entropy_eps = 1e-4
-seed = 0
 
 ######################################################################
 # Data collection parameters
@@ -154,6 +149,12 @@
 # too many frames will hamper training by reducing the reactivity of the actor
 # to observation changes.
 #
+# If we want to be honest, when using ``frame_skip`` it is good practice to
+# correct the other frame counts by the number of frames we are grouping
+# together. If we allow ourselves a total count of X frames for training but
+# use a frame_skip of Y, we will be actually collecting XY frames in total
+# which exceeds our predefined budget.
+#
 frame_skip = 1
 frames_per_batch = 1000 // frame_skip
 # For a complete training, bring the number of frames up to 1M
@@ -174,6 +175,9 @@
 sub_batch_size = 64  # total batch-size to compute the loss
 num_epochs = 10  # optimisation steps per batch of data collected
 clip_epsilon = 0.2  # clip value for PPO loss: see the equation in the intro for more context.
+gamma = 0.99
+lmbda = 0.95
+entropy_eps = 1e-4
 
 ######################################################################
 # Define an environment
@@ -192,8 +196,6 @@
     "InvertedDoublePendulum-v4", device=device,
     frame_skip=frame_skip
 )
-torch.manual_seed(seed)
-base_env.set_seed(seed)
 
 ######################################################################
 # There are already a few things to notice in this code: first, we created
@@ -479,7 +481,6 @@
     split_trajs=False,
     device=device,
 )
-collector.set_seed(seed)
 
 ######################################################################
 # Replay buffer

From dc23b91ee2956c94a416ea090ff4209c16e53c60 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Wed, 8 Feb 2023 15:27:01 +0000
Subject: [PATCH 26/52] amend

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index b1e1fdcbb01..dd8a1723160 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -172,7 +172,7 @@
 # we will further split in smaller sub-batches during the inner training loop.
 # The size of these sub-batches is controlled by ``sub_batch_size``.
 #
-sub_batch_size = 64  # total batch-size to compute the loss
+sub_batch_size = 64  # cardinality of the sub-samples gathered from the current data in the inner loop
 num_epochs = 10  # optimisation steps per batch of data collected
 clip_epsilon = 0.2  # clip value for PPO loss: see the equation in the intro for more context.
 gamma = 0.99

From bf594c372c72d26d1b411c5ae2e15c15d1719a91 Mon Sep 17 00:00:00 2001
From: Vincent Moens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 10:50:37 +0000
Subject: [PATCH 27/52] Update intermediate_source/reinforcement_ppo.py

Co-authored-by: Svetlana Karslioglu <svekars@fb.com>
---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index dd8a1723160..1ccf5fbf63a 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -4,7 +4,7 @@
 ==================================================
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
-This tutorial shows how to use PyTorch and TorchRL to train a parametric policy
+This tutorial demonstrates how to use PyTorch and TorchRL to train a parametric policy
 network to solve the Ant task from the OpenAI-Gym/Farama-Gymnasium control library
 https://github.com/Farama-Foundation/Gymnasium.
 

From 9e29ae0fe6ee82e214ef23a43dd5463026d6cc02 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:02:42 +0000
Subject: [PATCH 28/52] some suggestions

---
 intermediate_source/reinforcement_ppo.py | 47 +++++++++++++++---------
 1 file changed, 30 insertions(+), 17 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 1ccf5fbf63a..ee8b7edec23 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -5,41 +5,54 @@
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
 This tutorial demonstrates how to use PyTorch and TorchRL to train a parametric policy
-network to solve the Ant task from the OpenAI-Gym/Farama-Gymnasium control library
-https://github.com/Farama-Foundation/Gymnasium.
+network to solve the Inverted Pendulum task from the OpenAI-Gym/Farama-Gymnasium
+control library `<https://github.com/Farama-Foundation/Gymnasium>`__.
 
 .. figure:: /_static/img/invpendulum.gif
    :alt: Inverted pendulum
 
    Inverted pendulum
 
-Key learning items:
+Key learnings:
+
 - How to create an environment in TorchRL, transform its outputs, and collect data from this env;
 - How to make your classes talk to each other using :class:`TensorDict`;
 - The basics of building your training loop with TorchRL:
+
   - How to compute the advantage signal for policy gradient methods;
   - How to create a stochastic policy using a probabilistic neural network;
   - How to create a dynamic replay buffer and sample from it without repetition.
 
-We will cover six crucial components of TorchRL: environments, transforms,
-models (policy and value function), loss modules, data collectors and replay buffers.
+We will cover six crucial components of TorchRL:
+
+* `environments <https://pytorch.org/rl/reference/envs.html>`__
+* `transforms <https://pytorch.org/rl/reference/envs.html#transforms>`__
+* `models (policy and value function) <>`__
+* `loss modules <https://pytorch.org/rl/reference/modules.html>`__
+* `data collectors <https://pytorch.org/rl/reference/collectors.html>`__
+* `replay buffers <https://pytorch.org/rl/reference/data.html#replay-buffers>`__
 
+If you are running this in Google Colab, make sure you install the following dependencies:
 .. code-block:: bash
 
-   pip3 install torchrl
-   pip3 install gym[mujoco]
-   pip3 install tqdm
+   !pip3 install torchrl
+   !pip3 install gym[mujoco]
+   !pip3 install tqdm
 
 """
 
 ######################################################################
 #
-# Proximal Policy Optimization (PPO) is a policy-gradient algorithm (think of it
-# as an elaborated version of REINFORCE) where a batch of data is being
-# collected and directly consumed to train the policy to maximise
-# the expected return given some proximality constraints.
-#
-# Ref: https://arxiv.org/abs/1707.06347
+# Proximal Policy Optimization (PPO) is a policy-gradient algorithm where a batch of data is
+# collected and directly consumed to train the policy to maximiZe
+# the expected return given some proximality constraints. You can think of it
+# as an elaborated version of REINFORCE. For more information, see the Proximal Policy Optimization Algorithms <https://arxiv.org/abs/1707.06347>__ paper.
+
+# Proximal Policy Optimization (PPO) is a policy-gradient algorithm where a
+# batch of data is being collected and directly consumed to train the policy to maximise
+# the expected return given some proximality constraints. You can think of it
+# as a sophisticated version of REINFORCE. For more information, see the
+# `Proximal Policy Optimization Algorithms <https://arxiv.org/abs/1707.06347>`__ paper.
 #
 # PPO is usually regarded as a fast and efficient method for online, on-policy
 # reinforcement algorithm. TorchRL provides a loss-module that does all the work
@@ -81,7 +94,7 @@
 # 2. Next, we will focus on creating our environment, or simulator, using TorchRL's
 #    wrappers and transforms.
 #
-# 3. Next, We will design the policy network and the value model,
+# 3. Next, we will design the policy network and the value model,
 #    which is indispensable to the loss function. These modules will be used
 #    to configure our loss module.
 #
@@ -89,7 +102,7 @@
 #
 # 5. Finally, we will run our training loop and analyze the results.
 #
-# Throughout this tutorial, we'll be using the :py:mod:`tensordict` library.
+# Throughout this tutorial, we'll be using the :py:mod:`tensordict <https://pytorch-labs.github.io/tensordict/>`__ library.
 # :class:`TensorDict` is the lingua franca of TorchRL: it helps us abstract
 # what a module reads and writes and care less about the specific data
 # description and more about the algorithm itself.
@@ -120,7 +133,7 @@
 # ---------------
 #
 # We set the hyperparameters for our algorithm. Depending on the resources
-# available, one may choose to execute the policy on CUDA or on another
+# available, one may choose to execute the policy on GPU or on another
 # device.
 # The ``frame_skip`` will control how for how many frames is a single
 # action being executed. The rest of the arguments that count frames

From dd1da1c161344b9355ca44b364f1499c8cd50d35 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:17:56 +0000
Subject: [PATCH 29/52] some suggestions

---
 intermediate_source/reinforcement_ppo.py | 86 ++++++++++++++----------
 1 file changed, 51 insertions(+), 35 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index ee8b7edec23..e330c1e8a21 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -51,7 +51,8 @@
 # Proximal Policy Optimization (PPO) is a policy-gradient algorithm where a
 # batch of data is being collected and directly consumed to train the policy to maximise
 # the expected return given some proximality constraints. You can think of it
-# as a sophisticated version of REINFORCE. For more information, see the
+# as a sophisticated version of `REINFORCE <https://link.springer.com/content/pdf/10.1007/BF00992696.pdf>`__,
+# the foundational policy-optimization algorithm. For more information, see the
 # `Proximal Policy Optimization Algorithms <https://arxiv.org/abs/1707.06347>`__ paper.
 #
 # PPO is usually regarded as a fast and efficient method for online, on-policy
@@ -60,13 +61,13 @@
 # problem rather than re-inventing the wheel every time you want to train a policy.
 #
 # For completeness, here is a brief overview of what the loss computes, even though
-# this is taken care of by our :class:`ClipPPOLoss` module:
-# The algorithm works as follows: we will sample a batch of data by playing the
-# policy in the environment for a given number of steps. Then, we will perform a
-# given number of optimization steps with random sub-samples of this batch using
+# this is taken care of by our :class:`ClipPPOLoss` module—the algorithm works as follows:
+# 1. we will sample a batch of data by playing the
+# policy in the environment for a given number of steps.
+# 2. Then, we will perform a given number of optimization steps with random sub-samples of this batch using
 # a clipped version of the REINFORCE loss.
-# The clipping will put a pessimistic bound on our loss: lower return estimates will
-# be favoured compared to higher ones.
+# 3. The clipping will put a pessimistic bound on our loss: lower return estimates will
+# be favored compared to higher ones.
 # The precise formula of the loss is:
 #
 # .. math::
@@ -77,7 +78,7 @@
 #     \right),
 #
 # There are two components in that loss: in the first part of the minimum operator,
-# we simply compute an importance-weighted version of the REINFORCE loss (i.e. a
+# we simply compute an importance-weighted version of the REINFORCE loss (for example, a
 # REINFORCE loss that we have corrected for the fact that the current policy
 # configuration lags the one that was used for the data collection).
 # The second part of that minimum operator is a similar loss where we have clipped
@@ -129,8 +130,8 @@
 from tqdm import tqdm
 
 ######################################################################
-# Hyperparameters
-# ---------------
+# Define Hyperparameters
+# ----------------------
 #
 # We set the hyperparameters for our algorithm. Depending on the resources
 # available, one may choose to execute the policy on GPU or on another
@@ -152,20 +153,20 @@
 #
 # When collecting data, we will be able to choose how big each batch will be
 # by defining a ``frames_per_batch`` parameter. We will also define how many
-# frames (ie number of interactions with the simulator) we allow ourselves to
+# frames (such as the number of interactions with the simulator) we will allow ourselves to
 # use. In general, the goal of an RL algorithm is to learn to solve the task
-# as fast as we can in terms of environment interactions: the lowere the ``total_frames``
+# as fast as it can in terms of environment interactions: the lower the ``total_frames``
 # the better.
 # We also define a ``frame_skip``: in some contexts, repeating the same action
 # multiple times over the course of a trajectory may be beneficial as it makes
-# the behaviour more consistent and less erratic. However, a "skipping"
+# the behavior more consistent and less erratic. However, "skipping"
 # too many frames will hamper training by reducing the reactivity of the actor
 # to observation changes.
 #
-# If we want to be honest, when using ``frame_skip`` it is good practice to
+# When using ``frame_skip`` it is good practice to
 # correct the other frame counts by the number of frames we are grouping
-# together. If we allow ourselves a total count of X frames for training but
-# use a frame_skip of Y, we will be actually collecting XY frames in total
+# together. If we configure a total count of X frames for training but
+# use a ``frame_skip`` of Y, we will be actually collecting XY frames in total
 # which exceeds our predefined budget.
 #
 frame_skip = 1
@@ -178,7 +179,7 @@
 # ~~~~~~~~~~~~~~
 #
 # At each data collection (or batch collection) we will run the optimization
-# over a certain number of "epochs", each time consuming the entire data we just
+# over a certain number of *epochs*, each time consuming the entire data we just
 # acquired in a nested training loop. Here, the ``sub_batch_size`` is different from the
 # ``frames_per_batch`` here above: recall that we are working with a "batch of data"
 # coming from our collector, which size is defined by ``frames_per_batch``, and that
@@ -196,7 +197,7 @@
 # Define an environment
 # ---------------------
 #
-# In RL, an 'environment' is usually the way we refer to a simulator or a
+# In RL, an *environment* is usually the way we refer to a simulator or a
 # control system. Various libraries provide simulation environments for reinforcement
 # learning, including Gymnasium (previously OpenAI Gym), DeepMind control suite, and
 # many others.
@@ -211,14 +212,14 @@
 )
 
 ######################################################################
-# There are already a few things to notice in this code: first, we created
+# There are a few things to notice in this code: first, we created
 # the environment by calling the ``GymEnv`` wrapper. If extra keyword arguments
 # are passed, they will be transmitted to the ``gym.make`` method, hence covering
 # the most common env construction commands.
 # Alternatively, one could also directly create a gym environment using ``gym.make(env_name, **kwargs)``
 # and wrap it in a `GymWrapper` class.
 #
-# Notice also the ``device`` argument: for gym, this only controls the device where
+# Also the ``device`` argument: for gym, this only controls the device where
 # input action and observered states will be stored, but the execution will always
 # be done on CPU. The reason for this is simply that gym does not support on-device
 # execution, unless specified otherwise. For other libraries, we have control over
@@ -226,7 +227,7 @@
 # storing and execution backends.
 #
 # Transforms
-# ----------
+# ~~~~~~~~~~
 #
 # We will append some transforms to our environments to prepare the data for
 # the policy. In Gym, this is usually achieved via wrappers. TorchRL takes a different
@@ -237,7 +238,7 @@
 # of transforms it contains.
 #
 # Normalization
-# -------------
+# ~~~~~~~~~~~~~
 #
 # The first to encode is a normalization transform.
 # As a rule of thumbs, it is preferable to have data that loosely
@@ -525,7 +526,7 @@
 # -------------
 #
 # The PPO loss can be directly imported from torchrl for convenience using the
-# :class:`ClipPPOLoss` class. This is the easiest way of utilising PPO:
+# :class:`ClipPPOLoss` class. This is the easiest way of utilizing PPO:
 # it hides away the mathematical operations of PPO and the control flow that
 # goes with it.
 #
@@ -533,7 +534,7 @@
 # is a value that reflects an expectancy over the return value while dealing with
 # the bias / variance tradeoff.
 # To compute the advantage, one just needs to (1) build the advantage module, which
-# utilises our value operator, and (2) pass each batch of data through it before each
+# utilizes our value operator, and (2) pass each batch of data through it before each
 # epoch.
 # The GAE module will update the input tensordict with new ``"advantage"`` and
 # ``"value_target"`` entries.
@@ -573,8 +574,16 @@
 # Training loop
 # -------------
 # We now have all the pieces needed to code our training loop.
-# The steps are quite easy: collect data, compute advantage, loop over the collected
-# data to compute loss values, backpropagate, optimize and repeat.
+# The steps include:
+#
+# * Collect data
+#   * Compute advantage
+#     * Loop over the collected to compute loss values
+#     * Back propagate
+#     * Optimize
+#     * Repeat
+#   * Repeat
+# * Repeat
 #
 
 
@@ -675,15 +684,22 @@
 plt.show()
 
 ######################################################################
-# Next steps
-# ----------
+# Conclusion and next steps
+# -------------------------
+#
+# In this tutorial, we have learned:
+# 1.
+# 2.
+# 3.
+#
+# If you want to experiment with this tutorial a bit more, you can apply the following modifications:
 #
-# This algorithm could be improved in several way: from an efficiency perspective,
-# we could run several simulations in parallel to speed up data collection.
-# Check :obj:`torchrl.envs.ParallelEnv` for further information.
+# * From an efficiency perspective,
+#   we could run several simulations in parallel to speed up data collection.
+#   Check :obj:`torchrl.envs.ParallelEnv` for further information.
 #
-# From a logging perspective, one could add a :class:`VideoRecorder` transform to
-# the environment after asking for rendering to get a visual rendering of the
-# inverted pendulum in action. Check :obj:`torchrl.record.VideoRecorder` to
-# know more.
+# * From a logging perspective, one could add a :class:`VideoRecorder` transform to
+#   the environment after asking for rendering to get a visual rendering of the
+#   inverted pendulum in action. Check :obj:`torchrl.record.VideoRecorder` to
+#   know more.
 #

From 9c01de7d8952a1233f83a438d916f5d361f4ec3e Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:25:37 +0000
Subject: [PATCH 30/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index e330c1e8a21..e4e357c8307 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -5,8 +5,8 @@
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
 This tutorial demonstrates how to use PyTorch and TorchRL to train a parametric policy
-network to solve the Inverted Pendulum task from the OpenAI-Gym/Farama-Gymnasium
-control library `<https://github.com/Farama-Foundation/Gymnasium>`__.
+network to solve the Inverted Pendulum task from the `OpenAI-Gym/Farama-Gymnasium
+control library <https://github.com/Farama-Foundation/Gymnasium>`__.
 
 .. figure:: /_static/img/invpendulum.gif
    :alt: Inverted pendulum
@@ -27,8 +27,8 @@
 
 * `environments <https://pytorch.org/rl/reference/envs.html>`__
 * `transforms <https://pytorch.org/rl/reference/envs.html#transforms>`__
-* `models (policy and value function) <>`__
-* `loss modules <https://pytorch.org/rl/reference/modules.html>`__
+* `models (policy and value function) <https://pytorch.org/rl/reference/modules.html>`__
+* `loss modules <https://pytorch.org/rl/reference/objectives.html>`__
 * `data collectors <https://pytorch.org/rl/reference/collectors.html>`__
 * `replay buffers <https://pytorch.org/rl/reference/data.html#replay-buffers>`__
 

From 0f02ea3ad13a9160cfd23cc27483bb6b881b3553 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:25:58 +0000
Subject: [PATCH 31/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index e4e357c8307..d6a9504ee5a 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -42,7 +42,6 @@
 """
 
 ######################################################################
-#
 # Proximal Policy Optimization (PPO) is a policy-gradient algorithm where a batch of data is
 # collected and directly consumed to train the policy to maximiZe
 # the expected return given some proximality constraints. You can think of it

From abd7fe8ffff4f4544f8ae846405b2aa7816c7735 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:26:08 +0000
Subject: [PATCH 32/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index d6a9504ee5a..e8803f3c8cf 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -33,6 +33,7 @@
 * `replay buffers <https://pytorch.org/rl/reference/data.html#replay-buffers>`__
 
 If you are running this in Google Colab, make sure you install the following dependencies:
+
 .. code-block:: bash
 
    !pip3 install torchrl

From 5d63fdb1be7025f9bc66707bb8612279f5afcf33 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:30:26 +0000
Subject: [PATCH 33/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index e8803f3c8cf..730b387171d 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -32,17 +32,17 @@
 * `data collectors <https://pytorch.org/rl/reference/collectors.html>`__
 * `replay buffers <https://pytorch.org/rl/reference/data.html#replay-buffers>`__
 
-If you are running this in Google Colab, make sure you install the following dependencies:
-
-.. code-block:: bash
-
-   !pip3 install torchrl
-   !pip3 install gym[mujoco]
-   !pip3 install tqdm
-
 """
 
 ######################################################################
+# If you are running this in Google Colab, make sure you install the following dependencies:
+#
+# .. code-block:: bash
+#
+#    !pip3 install torchrl
+#    !pip3 install gym[mujoco]
+#    !pip3 install tqdm
+#
 # Proximal Policy Optimization (PPO) is a policy-gradient algorithm where a batch of data is
 # collected and directly consumed to train the policy to maximiZe
 # the expected return given some proximality constraints. You can think of it

From f3a85a03569a097837234361ea90fa0e8d1050ea Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:35:07 +0000
Subject: [PATCH 34/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 730b387171d..d93e74a27ae 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -43,17 +43,12 @@
 #    !pip3 install gym[mujoco]
 #    !pip3 install tqdm
 #
-# Proximal Policy Optimization (PPO) is a policy-gradient algorithm where a batch of data is
-# collected and directly consumed to train the policy to maximiZe
-# the expected return given some proximality constraints. You can think of it
-# as an elaborated version of REINFORCE. For more information, see the Proximal Policy Optimization Algorithms <https://arxiv.org/abs/1707.06347>__ paper.
-
 # Proximal Policy Optimization (PPO) is a policy-gradient algorithm where a
 # batch of data is being collected and directly consumed to train the policy to maximise
 # the expected return given some proximality constraints. You can think of it
-# as a sophisticated version of `REINFORCE <https://link.springer.com/content/pdf/10.1007/BF00992696.pdf>`__,
+# as a sophisticated version of `REINFORCE <https://link.springer.com/content/pdf/10.1007/BF00992696.pdf>`_,
 # the foundational policy-optimization algorithm. For more information, see the
-# `Proximal Policy Optimization Algorithms <https://arxiv.org/abs/1707.06347>`__ paper.
+# `Proximal Policy Optimization Algorithms <https://arxiv.org/abs/1707.06347>`_ paper.
 #
 # PPO is usually regarded as a fast and efficient method for online, on-policy
 # reinforcement algorithm. TorchRL provides a loss-module that does all the work

From 3e514a1e85cca3656cbe3b0d5476158cc0026a2b Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:44:25 +0000
Subject: [PATCH 35/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index d93e74a27ae..034291151d2 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -98,7 +98,7 @@
 #
 # 5. Finally, we will run our training loop and analyze the results.
 #
-# Throughout this tutorial, we'll be using the :py:mod:`tensordict <https://pytorch-labs.github.io/tensordict/>`__ library.
+# Throughout this tutorial, we'll be using the :py:mod:`tensordict <https://pytorch-labs.github.io/tensordict/>`_ library.
 # :class:`TensorDict` is the lingua franca of TorchRL: it helps us abstract
 # what a module reads and writes and care less about the specific data
 # description and more about the algorithm itself.

From a6132d25dcab805e86af49d49aeec5ab94314914 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:49:44 +0000
Subject: [PATCH 36/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 034291151d2..6b6051d59b9 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -98,7 +98,7 @@
 #
 # 5. Finally, we will run our training loop and analyze the results.
 #
-# Throughout this tutorial, we'll be using the :py:mod:`tensordict <https://pytorch-labs.github.io/tensordict/>`_ library.
+# Throughout this tutorial, we'll be using the :py:mod:`tensordict` <https://pytorch-labs.github.io/tensordict/>` library.
 # :class:`TensorDict` is the lingua franca of TorchRL: it helps us abstract
 # what a module reads and writes and care less about the specific data
 # description and more about the algorithm itself.

From ab6c4e38fd22f11a3581b5deecb534e779e829b5 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:55:43 +0000
Subject: [PATCH 37/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 6b6051d59b9..91ed36e3f36 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -98,7 +98,7 @@
 #
 # 5. Finally, we will run our training loop and analyze the results.
 #
-# Throughout this tutorial, we'll be using the :py:mod:`tensordict` <https://pytorch-labs.github.io/tensordict/>` library.
+# Throughout this tutorial, we'll be using the `:py:mod:`tensordict` <https://pytorch-labs.github.io/tensordict/>` library.
 # :class:`TensorDict` is the lingua franca of TorchRL: it helps us abstract
 # what a module reads and writes and care less about the specific data
 # description and more about the algorithm itself.

From 3e281e15b2dc011d35130fee3b40c3578029a894 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 11:56:34 +0000
Subject: [PATCH 38/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 91ed36e3f36..e0a5b816866 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -572,12 +572,16 @@
 # The steps include:
 #
 # * Collect data
+#
 #   * Compute advantage
+#
 #     * Loop over the collected to compute loss values
 #     * Back propagate
 #     * Optimize
 #     * Repeat
+#
 #   * Repeat
+#
 # * Repeat
 #
 

From 60da0b130cad8122f75463de8250afc3cf03d71f Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 13:20:34 +0000
Subject: [PATCH 39/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index e0a5b816866..c4701f83c01 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -98,7 +98,7 @@
 #
 # 5. Finally, we will run our training loop and analyze the results.
 #
-# Throughout this tutorial, we'll be using the `:py:mod:`tensordict` <https://pytorch-labs.github.io/tensordict/>` library.
+# Throughout this tutorial, we'll be using the :mod:`tensordict` library.
 # :class:`TensorDict` is the lingua franca of TorchRL: it helps us abstract
 # what a module reads and writes and care less about the specific data
 # description and more about the algorithm itself.

From 15dbc0cf3dd1efb0428c701bef54c86ec6b40505 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 13:22:00 +0000
Subject: [PATCH 40/52] rendering

---
 conf.py                                  | 2 ++
 intermediate_source/reinforcement_ppo.py | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/conf.py b/conf.py
index bd949b12848..5c78af01db5 100644
--- a/conf.py
+++ b/conf.py
@@ -76,6 +76,8 @@
 
 intersphinx_mapping = {
     "torch": ("https://pytorch.org/docs/stable/", None),
+    "tensordict": ("https://pytorch-labs.github.io/tensordict/", None),
+    "torchrl": ("https://pytorch.org/rl/", None),
     "torchaudio": ("https://pytorch.org/audio/stable/", None),
     "torchtext": ("https://pytorch.org/text/stable/", None),
     "torchvision": ("https://pytorch.org/vision/stable/", None),
diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index c4701f83c01..0f1038897db 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -4,7 +4,7 @@
 ==================================================
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
-This tutorial demonstrates how to use PyTorch and TorchRL to train a parametric policy
+This tutorial demonstrates how to use PyTorch and :mod:`torchrl` to train a parametric policy
 network to solve the Inverted Pendulum task from the `OpenAI-Gym/Farama-Gymnasium
 control library <https://github.com/Farama-Foundation/Gymnasium>`__.
 

From f566e3a492dc256337e63f9e4a3f6f9f750144ab Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 13:52:51 +0000
Subject: [PATCH 41/52] rendering

---
 intermediate_source/reinforcement_ppo.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 0f1038897db..9df24f55717 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -4,7 +4,7 @@
 ==================================================
 **Author**: `Vincent Moens <https://github.com/vmoens>`_
 
-This tutorial demonstrates how to use PyTorch and :mod:`torchrl` to train a parametric policy
+This tutorial demonstrates how to use PyTorch and :py:mod:`torchrl` to train a parametric policy
 network to solve the Inverted Pendulum task from the `OpenAI-Gym/Farama-Gymnasium
 control library <https://github.com/Farama-Foundation/Gymnasium>`__.
 
@@ -98,7 +98,7 @@
 #
 # 5. Finally, we will run our training loop and analyze the results.
 #
-# Throughout this tutorial, we'll be using the :mod:`tensordict` library.
+# Throughout this tutorial, we'll be using the :py:mod:`tensordict` library.
 # :class:`TensorDict` is the lingua franca of TorchRL: it helps us abstract
 # what a module reads and writes and care less about the specific data
 # description and more about the algorithm itself.
@@ -687,18 +687,18 @@
 # -------------------------
 #
 # In this tutorial, we have learned:
-# 1.
-# 2.
-# 3.
+# 1. How to create and customize an environment with :py:mod:`torchrl`;
+# 2. How to write a model and a loss function;
+# 3. How to set up a typical training loop.
 #
 # If you want to experiment with this tutorial a bit more, you can apply the following modifications:
 #
 # * From an efficiency perspective,
 #   we could run several simulations in parallel to speed up data collection.
-#   Check :obj:`torchrl.envs.ParallelEnv` for further information.
+#   Check :class:`torchrl.envs.ParallelEnv` for further information.
 #
-# * From a logging perspective, one could add a :class:`VideoRecorder` transform to
+# * From a logging perspective, one could add a :class:`torchrl.record.VideoRecorder` transform to
 #   the environment after asking for rendering to get a visual rendering of the
-#   inverted pendulum in action. Check :obj:`torchrl.record.VideoRecorder` to
+#   inverted pendulum in action. Check :obj:`torchrl.record` to
 #   know more.
 #

From 33bc95bda3b9320eb6a9550eeaf9fd4018a0804d Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 14:01:58 +0000
Subject: [PATCH 42/52] add link

---
 intermediate_source/reinforcement_ppo.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 9df24f55717..b19354475c8 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -98,8 +98,8 @@
 #
 # 5. Finally, we will run our training loop and analyze the results.
 #
-# Throughout this tutorial, we'll be using the :py:mod:`tensordict` library.
-# :class:`TensorDict` is the lingua franca of TorchRL: it helps us abstract
+# Throughout this tutorial, we'll be using the :mod:`tensordict` library.
+# :class:`tensordict.TensorDict` is the lingua franca of TorchRL: it helps us abstract
 # what a module reads and writes and care less about the specific data
 # description and more about the algorithm itself.
 #

From 9ea4f84686f83c691745d451664fb94d28339484 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 14:18:19 +0000
Subject: [PATCH 43/52] add link

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index b19354475c8..f6f452ca5a0 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -699,6 +699,6 @@
 #
 # * From a logging perspective, one could add a :class:`torchrl.record.VideoRecorder` transform to
 #   the environment after asking for rendering to get a visual rendering of the
-#   inverted pendulum in action. Check :obj:`torchrl.record` to
+#   inverted pendulum in action. Check :py:mod:`torchrl.record` to
 #   know more.
 #

From b220d005950a059c1e46164314e5852ee67b0dc9 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 14:21:03 +0000
Subject: [PATCH 44/52] add link

---
 intermediate_source/reinforcement_ppo.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index f6f452ca5a0..b2828235a66 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -16,7 +16,7 @@
 Key learnings:
 
 - How to create an environment in TorchRL, transform its outputs, and collect data from this env;
-- How to make your classes talk to each other using :class:`TensorDict`;
+- How to make your classes talk to each other using :class:`tensordict.TensorDict`;
 - The basics of building your training loop with TorchRL:
 
   - How to compute the advantage signal for policy gradient methods;
@@ -247,7 +247,7 @@
 # the environment is terminated. We will use this measure as a supplementary measure
 # of performance.
 #
-# As we will see later, many of the TorchRL's classes rely on :class:`TensorDict`
+# As we will see later, many of the TorchRL's classes rely on :class:`tensordict.TensorDict`
 # to communicate. You could think of it as a python dictionary with some extra
 # tensor features. In practice, this means that many modules we will be working
 # with need to be told what key to read (``in_keys``) and what key to write
@@ -323,9 +323,9 @@
 # action as input, and outputs an observation, a reward and a done state. The
 # observation may be composite, meaning that it could be composed of more than one
 # tensor. This is not a problem for TorchRL, since the whole set of observations
-# is automatically packed in the output :class:`TensorDict`. After executing a rollout
+# is automatically packed in the output :class:`tensordict.TensorDict`. After executing a rollout
 # (ie a sequence of environment steps and random action generations) over a given
-# number of steps, we will retrieve a :class:`TensorDict` instance with a shape
+# number of steps, we will retrieve a :class:`tensordict.TensorDict` instance with a shape
 # that matches this trajectory length:
 #
 rollout = env.rollout(3)
@@ -477,8 +477,8 @@
 # and asynchronous manner over a set of multiprocessed workers.
 #
 # As for the policy and environment before, the data collector will return
-# :class:`TensorDict` instances with a total number of elements that will
-# match ``frames_per_batch``. Using :class:`TensorDict` to pass data to the
+# :class:`tensordict.TensorDict` instances with a total number of elements that will
+# match ``frames_per_batch``. Using :class:`tensordict.TensorDict` to pass data to the
 # training loop allows you to write dataloading pipelines
 # that are 100% oblivious to the actual specificities of the rollout content.
 #

From bf00120733eef439fc44493f314b9ffb6b09f697 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 14:23:14 +0000
Subject: [PATCH 45/52] minor

---
 intermediate_source/reinforcement_ppo.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index b2828235a66..260fa9d7994 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -687,6 +687,7 @@
 # -------------------------
 #
 # In this tutorial, we have learned:
+#
 # 1. How to create and customize an environment with :py:mod:`torchrl`;
 # 2. How to write a model and a loss function;
 # 3. How to set up a typical training loop.

From b334189a65383ee1c86e3b02c3a318dd26011979 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Thu, 9 Feb 2023 21:17:44 +0000
Subject: [PATCH 46/52] fix

---
 intermediate_source/reinforcement_ppo.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 260fa9d7994..101adb9ca28 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -330,7 +330,7 @@
 #
 rollout = env.rollout(3)
 print("rollout of three steps:", rollout)
-print("Shape of the rollout TensorDict:", rollout.sub_batch_size)
+print("Shape of the rollout TensorDict:", rollout.batch_size)
 
 ######################################################################
 # Our rollout data has a shape of ``torch.Size([3])`, which matches the number of steps

From 63e62595055a497762126dfc0d5b4a0d9a55fc93 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Sat, 11 Feb 2023 22:46:57 +0100
Subject: [PATCH 47/52] empty

---
 _static/img/profiler_overview1.png   | Bin 96035 -> 0 bytes
 _static/img/profiler_overview2.png   | Bin 82556 -> 0 bytes
 _static/img/profiler_trace_view1.png | Bin 149843 -> 0 bytes
 _static/img/profiler_trace_view2.png | Bin 128674 -> 0 bytes
 _static/img/profiler_views_list.png  | Bin 52562 -> 0 bytes
 5 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 _static/img/profiler_overview1.png
 delete mode 100644 _static/img/profiler_overview2.png
 delete mode 100644 _static/img/profiler_trace_view1.png
 delete mode 100644 _static/img/profiler_trace_view2.png
 delete mode 100644 _static/img/profiler_views_list.png

diff --git a/_static/img/profiler_overview1.png b/_static/img/profiler_overview1.png
deleted file mode 100644
index 01eef8fda68ccf6a55545ce6bfbb82e264e52795..0000000000000000000000000000000000000000
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From 15c8c61a4e35d3d79995a55c9cf0983fa648f760 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Sat, 11 Feb 2023 22:50:23 +0100
Subject: [PATCH 48/52] merge from main

---
 _static/img/profiler_overview1.png   | Bin 0 -> 96035 bytes
 _static/img/profiler_overview2.png   | Bin 0 -> 82556 bytes
 _static/img/profiler_trace_view1.png | Bin 0 -> 149843 bytes
 _static/img/profiler_trace_view2.png | Bin 0 -> 128674 bytes
 _static/img/profiler_views_list.png  | Bin 0 -> 52562 bytes
 index.rst                            |   4 ++--
 6 files changed, 2 insertions(+), 2 deletions(-)
 create mode 100644 _static/img/profiler_overview1.png
 create mode 100644 _static/img/profiler_overview2.png
 create mode 100644 _static/img/profiler_trace_view1.png
 create mode 100644 _static/img/profiler_trace_view2.png
 create mode 100644 _static/img/profiler_views_list.png

diff --git a/_static/img/profiler_overview1.png b/_static/img/profiler_overview1.png
new file mode 100644
index 0000000000000000000000000000000000000000..01eef8fda68ccf6a55545ce6bfbb82e264e52795
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literal 0
HcmV?d00001

diff --git a/index.rst b/index.rst
index cca477d9068..d9751e2887b 100644
--- a/index.rst
+++ b/index.rst
@@ -271,8 +271,8 @@ What's new in PyTorch tutorials?
 
 .. customcarditem::
    :header: Reinforcement Learning (PPO) with TorchRL
-   :card_description: Learn how to use PyTorch and TorchRL to train a Proximal Policy Optimization agent on the cheetah-run task from dm-control.
-   :image: _static/img/cheetah.gif
+   :card_description: Learn how to use PyTorch and TorchRL to train a Proximal Policy Optimization agent on the Inverted Pendulum task from Gym.
+   :image: _static/img/invpendulum.gif
    :link: intermediate/reinforcement_ppo.html
    :tags: Reinforcement-Learning
 

From d800302f4ade14f8416d5ac32e42a299382b1adf Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Sat, 11 Feb 2023 23:20:35 +0100
Subject: [PATCH 49/52] empty


From eb53a6cbc1694f2cc0f131ade4b2afbf90a99479 Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Sun, 12 Feb 2023 17:59:25 +0100
Subject: [PATCH 50/52] device bugfix

---
 intermediate_source/reinforcement_ppo.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index 101adb9ca28..b450b56c4c7 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -600,10 +600,10 @@
         # network which is updated in the inner loop.
         advantage_module(tensordict_data)
         data_view = tensordict_data.reshape(-1)
-        replay_buffer.extend(data_view)
+        replay_buffer.extend(data_view.cpu())
         for j in range(frames_per_batch // sub_batch_size):
             subdata, *_ = replay_buffer.sample(sub_batch_size)
-            loss_vals = loss_module(subdata)
+            loss_vals = loss_module(subdata.to(device))
             loss_value = loss_vals["loss_objective"] + loss_vals[
                 "loss_critic"] + loss_vals["loss_entropy"]
 

From d0fe4180afb2e4071af1e9794492fafeaf0e4aff Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Mon, 6 Mar 2023 17:33:49 +0000
Subject: [PATCH 51/52] gymnasium bugfix

---
 requirements.txt | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 20605994908..321ce5a3ca9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -51,10 +51,9 @@ scikit-image
 scipy
 pillow==9.3.0
 wget
-gymnasium[mujoco]==0.26.1
 gym-super-mario-bros==7.4.0
 pyopengl
-gymnasium==0.27.0
+gymnasium[mujoco]==0.27.0
 timm
 iopath
 pygame==2.1.2

From e65806f5c67684070bd00766cee0f197072e2ced Mon Sep 17 00:00:00 2001
From: vmoens <vincentmoens@gmail.com>
Date: Wed, 8 Mar 2023 21:29:02 +0000
Subject: [PATCH 52/52] formatting

---
 intermediate_source/reinforcement_ppo.py | 91 ++++++++++++------------
 1 file changed, 44 insertions(+), 47 deletions(-)

diff --git a/intermediate_source/reinforcement_ppo.py b/intermediate_source/reinforcement_ppo.py
index b450b56c4c7..6b0e8522f8d 100644
--- a/intermediate_source/reinforcement_ppo.py
+++ b/intermediate_source/reinforcement_ppo.py
@@ -115,11 +115,16 @@
 from torchrl.data.replay_buffers import ReplayBuffer
 from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement
 from torchrl.data.replay_buffers.storages import LazyTensorStorage
-from torchrl.envs import TransformedEnv, ObservationNorm, Compose, \
-    DoubleToFloat, StepCounter
+from torchrl.envs import (
+    Compose,
+    DoubleToFloat,
+    ObservationNorm,
+    StepCounter,
+    TransformedEnv,
+)
 from torchrl.envs.libs.gym import GymEnv
-from torchrl.envs.utils import set_exploration_mode, check_env_specs
-from torchrl.modules import TanhNormal, ProbabilisticActor, ValueOperator
+from torchrl.envs.utils import check_env_specs, set_exploration_mode
+from torchrl.modules import ProbabilisticActor, TanhNormal, ValueOperator
 from torchrl.objectives import ClipPPOLoss
 from torchrl.objectives.value import GAE
 from tqdm import tqdm
@@ -183,7 +188,9 @@
 #
 sub_batch_size = 64  # cardinality of the sub-samples gathered from the current data in the inner loop
 num_epochs = 10  # optimisation steps per batch of data collected
-clip_epsilon = 0.2  # clip value for PPO loss: see the equation in the intro for more context.
+clip_epsilon = (
+    0.2  # clip value for PPO loss: see the equation in the intro for more context.
+)
 gamma = 0.99
 lmbda = 0.95
 entropy_eps = 1e-4
@@ -201,10 +208,7 @@
 # with another. For example, creating a wrapped gym environment can be achieved with few characters:
 #
 
-base_env = GymEnv(
-    "InvertedDoublePendulum-v4", device=device,
-    frame_skip=frame_skip
-)
+base_env = GymEnv("InvertedDoublePendulum-v4", device=device, frame_skip=frame_skip)
 
 ######################################################################
 # There are a few things to notice in this code: first, we created
@@ -263,9 +267,9 @@
     Compose(
         # normalize observations
         ObservationNorm(in_keys=["observation"]),
-        DoubleToFloat(in_keys=["observation"], ),
+        DoubleToFloat(in_keys=["observation"]),
         StepCounter(),
-    )
+    ),
 )
 
 ######################################################################
@@ -386,8 +390,7 @@
 # outputs in-place at the registered ``out_keys``.
 #
 policy_module = TensorDictModule(
-    actor_net, in_keys=["observation"],
-    out_keys=["loc", "scale"]
+    actor_net, in_keys=["observation"], out_keys=["loc", "scale"]
 )
 
 ######################################################################
@@ -410,8 +413,10 @@
     spec=env.action_spec,
     in_keys=["loc", "scale"],
     distribution_class=TanhNormal,
-    distribution_kwargs={"min": env.action_spec.space.minimum,
-                         "max": env.action_spec.space.maximum},
+    distribution_kwargs={
+        "min": env.action_spec.space.minimum,
+        "max": env.action_spec.space.maximum,
+    },
     return_log_prob=True,
     # we'll need the log-prob for the numerator of the importance weights
 )
@@ -540,8 +545,7 @@
 #
 
 advantage_module = GAE(
-    gamma=gamma, lmbda=lmbda, value_network=value_module,
-    average_gae=True
+    gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True
 )
 
 loss_module = ClipPPOLoss(
@@ -560,9 +564,7 @@
 
 optim = torch.optim.Adam(loss_module.parameters(), lr)
 scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
-    optim,
-    total_frames // frames_per_batch,
-    0.0
+    optim, total_frames // frames_per_batch, 0.0
 )
 
 ######################################################################
@@ -594,36 +596,38 @@
 # designed to collect:
 for i, tensordict_data in enumerate(collector):
     # we now have a batch of data to work with. Let's learn something from it.
-    for k in range(num_epochs):
+    for _ in range(num_epochs):
         # We'll need an "advantage" signal to make PPO work.
         # We re-compute it at each epoch as its value depends on the value
         # network which is updated in the inner loop.
         advantage_module(tensordict_data)
         data_view = tensordict_data.reshape(-1)
         replay_buffer.extend(data_view.cpu())
-        for j in range(frames_per_batch // sub_batch_size):
+        for _ in range(frames_per_batch // sub_batch_size):
             subdata, *_ = replay_buffer.sample(sub_batch_size)
             loss_vals = loss_module(subdata.to(device))
-            loss_value = loss_vals["loss_objective"] + loss_vals[
-                "loss_critic"] + loss_vals["loss_entropy"]
+            loss_value = (
+                loss_vals["loss_objective"]
+                + loss_vals["loss_critic"]
+                + loss_vals["loss_entropy"]
+            )
 
             # Optimization: backward, grad clipping and optim step
             loss_value.backward()
             # this is not strictly mandatory but it's good practice to keep
             # your gradient norm bounded
-            torch.nn.utils.clip_grad_norm_(
-                loss_module.parameters(),
-                max_grad_norm
-            )
+            torch.nn.utils.clip_grad_norm_(loss_module.parameters(), max_grad_norm)
             optim.step()
             optim.zero_grad()
 
-    logs["reward"].append(tensordict_data["reward"].mean().item())
+    logs["reward"].append(tensordict_data["next", "reward"].mean().item())
     pbar.update(tensordict_data.numel() * frame_skip)
-    cum_reward_str = f"average reward={logs['reward'][-1]: 4.4f} (init={logs['reward'][0]: 4.4f})"
-    logs["step_count"].append(tensordict_data['step_count'].max().item())
+    cum_reward_str = (
+        f"average reward={logs['reward'][-1]: 4.4f} (init={logs['reward'][0]: 4.4f})"
+    )
+    logs["step_count"].append(tensordict_data["step_count"].max().item())
     stepcount_str = f"step count (max): {logs['step_count'][-1]}"
-    logs["lr"].append(optim.param_groups[0]['lr'])
+    logs["lr"].append(optim.param_groups[0]["lr"])
     lr_str = f"lr policy: {logs['lr'][-1]: 4.4f}"
     if i % 10 == 0:
         # We evaluate the policy once every 10 batches of data.
@@ -635,25 +639,18 @@
         with set_exploration_mode("mean"), torch.no_grad():
             # execute a rollout with the trained policy
             eval_rollout = env.rollout(1000, policy_module)
-            logs["eval reward"].append(eval_rollout["reward"].mean().item())
+            logs["eval reward"].append(eval_rollout["next", "reward"].mean().item())
             logs["eval reward (sum)"].append(
-                eval_rollout["reward"].sum().item()
+                eval_rollout["next", "reward"].sum().item()
             )
-            logs["eval step_count"].append(
-                eval_rollout["step_count"].max().item()
+            logs["eval step_count"].append(eval_rollout["step_count"].max().item())
+            eval_str = (
+                f"eval cumulative reward: {logs['eval reward (sum)'][-1]: 4.4f} "
+                f"(init: {logs['eval reward (sum)'][0]: 4.4f}), "
+                f"eval step-count: {logs['eval step_count'][-1]}"
             )
-            eval_str = f"eval cumulative reward: {logs['eval reward (sum)'][-1]: 4.4f} (init: {logs['eval reward (sum)'][0]: 4.4f}), eval step-count: {logs['eval step_count'][-1]}"
             del eval_rollout
-    pbar.set_description(
-        ", ".join(
-            [
-                eval_str,
-                cum_reward_str,
-                stepcount_str,
-                lr_str
-            ]
-        )
-    )
+    pbar.set_description(", ".join([eval_str, cum_reward_str, stepcount_str, lr_str]))
 
     # We're also using a learning rate scheduler. Like the gradient clipping,
     # this is a nice-to-have but nothing necessary for PPO to work.