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ppo.py
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ppo.py
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import os
import warnings
from typing import Any, Callable, Dict, Optional, Type, Union
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
#
import pandas as pd
import scipy
import torch as th
from gym import spaces
from mpl_toolkits.mplot3d import Axes3D
#
from stable_baselines3.common.policies import ActorCriticPolicy
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
from stable_baselines3.common.utils import explained_variance, get_schedule_fn
from torch.nn import functional as F
#
from flightrl.rpg_baselines.torch.common.on_policy_algorithm import \
OnPolicyAlgorithm
from flightrl.rpg_baselines.torch.common.util import plot3d_traj, traj_rollout
class PPO(OnPolicyAlgorithm):
"""
Proximal Policy Optimization algorithm (PPO) (clip version)
Paper: https://arxiv.org/abs/1707.06347
Code: This implementation borrows code from OpenAI Spinning Up (https://github.com/openai/spinningup/)
https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail and
and Stable Baselines (PPO2 from https://github.com/hill-a/stable-baselines)
Introduction to PPO: https://spinningup.openai.com/en/latest/algorithms/ppo.html
:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
:param env: The environment to learn from (if registered in Gym, can be str)
:param learning_rate: The learning rate, it can be a function
of the current progress remaining (from 1 to 0)
:param n_steps: The number of steps to run for each environment per update
(i.e. rollout buffer size is n_steps * n_envs where n_envs is number of environment copies running in parallel)
NOTE: n_steps * n_envs must be greater than 1 (because of the advantage normalization)
See https://github.com/pytorch/pytorch/issues/29372
:param batch_size: Minibatch size
:param n_epochs: Number of epoch when optimizing the surrogate loss
:param gamma: Discount factor
:param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator
:param clip_range: Clipping parameter, it can be a function of the current progress
remaining (from 1 to 0).
:param clip_range_vf: Clipping parameter for the value function,
it can be a function of the current progress remaining (from 1 to 0).
This is a parameter specific to the OpenAI implementation. If None is passed (default),
no clipping will be done on the value function.
IMPORTANT: this clipping depends on the reward scaling.
:param ent_coef: Entropy coefficient for the loss calculation
:param vf_coef: Value function coefficient for the loss calculation
:param max_grad_norm: The maximum value for the gradient clipping
:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
instead of action noise exploration (default: False)
:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
Default: -1 (only sample at the beginning of the rollout)
:param target_kl: Limit the KL divergence between updates,
because the clipping is not enough to prevent large update
see issue #213 (cf https://github.com/hill-a/stable-baselines/issues/213)
By default, there is no limit on the kl div.
:param tensorboard_log: the log location for tensorboard (if None, no logging)
:param create_eval_env: Whether to create a second environment that will be
used for evaluating the agent periodically. (Only available when passing string for the environment)
:param policy_kwargs: additional arguments to be passed to the policy on creation
:param verbose: the verbosity level: 0 no output, 1 info, 2 debug
:param seed: Seed for the pseudo random generators
:param device: Device (cpu, cuda, ...) on which the code should be run.
Setting it to auto, the code will be run on the GPU if possible.
:param _init_setup_model: Whether or not to build the network at the creation of the instance
"""
def __init__(
self,
policy: Union[str, Type[ActorCriticPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Callable] = 3e-4,
n_steps: int = 2048,
use_tanh_act: bool = True,
batch_size: Optional[int] = 64,
n_epochs: int = 10,
gamma: float = 0.99,
gae_lambda: float = 0.95,
clip_range: Union[float, Callable] = 0.2,
clip_range_vf: Union[None, float, Callable] = None,
ent_coef: float = 0.0,
vf_coef: float = 0.5,
max_grad_norm: float = 0.5,
use_sde: bool = False,
sde_sample_freq: int = -1,
target_kl: Optional[float] = None,
tensorboard_log: Optional[str] = None,
create_eval_env: bool = False,
eval_env: Union[GymEnv, str] = None,
policy_kwargs: Optional[Dict[str, Any]] = None,
verbose: int = 0,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
env_cfg: str = None,
_init_setup_model: bool = True,
):
super(PPO, self).__init__(
policy,
env,
learning_rate=learning_rate,
n_steps=n_steps,
gamma=gamma,
gae_lambda=gae_lambda,
ent_coef=ent_coef,
use_tanh_act=use_tanh_act,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
use_sde=use_sde,
sde_sample_freq=sde_sample_freq,
tensorboard_log=tensorboard_log,
policy_kwargs=policy_kwargs,
verbose=verbose,
device=device,
create_eval_env=create_eval_env,
eval_env=eval_env,
seed=seed,
_init_setup_model=False,
supported_action_spaces=(
spaces.Box,
spaces.Discrete,
spaces.MultiDiscrete,
spaces.MultiBinary,
),
)
if self.env is not None:
# Check that `n_steps * n_envs > 1` to avoid NaN
# when doing advantage normalization
buffer_size = self.env.num_envs * self.n_steps
assert (
buffer_size > 1
), f"`n_steps * n_envs` must be greater than 1. Currently n_steps={self.n_steps} and n_envs={self.env.num_envs}"
# Check that the rollout buffer size is a multiple of the mini-batch size
untruncated_batches = buffer_size // batch_size
if buffer_size % batch_size > 0:
warnings.warn(
f"You have specified a mini-batch size of {batch_size},"
f" but because the `RolloutBuffer` is of size `n_steps * n_envs = {buffer_size}`,"
f" after every {untruncated_batches} untruncated mini-batches,"
f" there will be a truncated mini-batch of size {buffer_size % batch_size}\n"
f"We recommend using a `batch_size` that is a multiple of `n_steps * n_envs`.\n"
f"Info: (n_steps={self.n_steps} and n_envs={self.env.num_envs})"
)
self.batch_size = batch_size
self.n_epochs = n_epochs
self.clip_range = clip_range
self.clip_range_vf = clip_range_vf
self.target_kl = target_kl
self.env_cfg = env_cfg
if _init_setup_model:
self._setup_model()
def _setup_model(self) -> None:
super(PPO, self)._setup_model()
# Initialize schedules for policy/value clipping
self.clip_range = get_schedule_fn(self.clip_range)
if self.clip_range_vf is not None:
if isinstance(self.clip_range_vf, (float, int)):
assert self.clip_range_vf > 0, (
"`clip_range_vf` must be positive, "
"pass `None` to deactivate vf clipping"
)
self.clip_range_vf = get_schedule_fn(self.clip_range_vf)
def train(self) -> None:
"""
Update policy using the currently gathered rollout buffer.
"""
# Update optimizer learning rate
self._update_learning_rate(self.policy.optimizer)
# Compute current clip range
clip_range = self.clip_range(self._current_progress_remaining)
# Optional: clip range for the value function
if self.clip_range_vf is not None:
clip_range_vf = self.clip_range_vf(self._current_progress_remaining)
entropy_losses, all_kl_divs = [], []
pg_losses, value_losses = [], []
clip_fractions = []
# train for n_epochs epochs
for epoch in range(self.n_epochs):
approx_kl_divs = []
# Do a complete pass on the rollout buffer
for rollout_data in self.rollout_buffer.get(self.batch_size):
actions = rollout_data.actions
if isinstance(self.action_space, spaces.Discrete):
# Convert discrete action from float to long
actions = rollout_data.actions.long().flatten()
# Re-sample the noise matrix because the log_std has changed
# TODO: investigate why there is no issue with the gradient
# if that line is commented (as in SAC)
if self.use_sde:
self.policy.reset_noise(self.batch_size)
values, log_prob, entropy = self.policy.evaluate_actions(
rollout_data.observations, actions
)
values = values.flatten()
# Normalize advantage
advantages = rollout_data.advantages
advantages = (advantages - advantages.mean()) / (
advantages.std() + 1e-8
)
# ratio between old and new policy, should be one at the first iteration
ratio = th.exp(log_prob - rollout_data.old_log_prob)
# clipped surrogate loss
policy_loss_1 = advantages * ratio
policy_loss_2 = advantages * th.clamp(
ratio, 1 - clip_range, 1 + clip_range
)
policy_loss = -th.min(policy_loss_1, policy_loss_2).mean()
# Logging
pg_losses.append(policy_loss.item())
clip_fraction = th.mean((th.abs(ratio - 1) > clip_range).float()).item()
clip_fractions.append(clip_fraction)
if self.clip_range_vf is None:
# No clipping
values_pred = values
else:
# Clip the different between old and new value
# NOTE: this depends on the reward scaling
values_pred = rollout_data.old_values + th.clamp(
values - rollout_data.old_values, -clip_range_vf, clip_range_vf
)
# Value loss using the TD(gae_lambda) target
value_loss = F.mse_loss(rollout_data.returns, values_pred)
value_losses.append(value_loss.item())
# Entropy loss favor exploration
if entropy is None:
# Approximate entropy when no analytical form
entropy_loss = -th.mean(-log_prob)
else:
entropy_loss = -th.mean(entropy)
entropy_losses.append(entropy_loss.item())
loss = (
policy_loss
+ self.ent_coef * entropy_loss
+ self.vf_coef * value_loss
)
# Optimization step
self.policy.optimizer.zero_grad()
loss.backward()
# Clip grad norm
th.nn.utils.clip_grad_norm_(
self.policy.parameters(), self.max_grad_norm
)
self.policy.optimizer.step()
approx_kl_divs.append(
th.mean(rollout_data.old_log_prob - log_prob).detach().cpu().numpy()
)
all_kl_divs.append(np.mean(approx_kl_divs))
if (
self.target_kl is not None
and np.mean(approx_kl_divs) > 1.5 * self.target_kl
):
print(
f"Early stopping at step {epoch} due to reaching max kl: {np.mean(approx_kl_divs):.2f}"
)
break
self._n_updates += self.n_epochs
explained_var = explained_variance(
self.rollout_buffer.values.flatten(), self.rollout_buffer.returns.flatten()
)
# Logs
self.logger.record("train/entropy_loss", np.mean(entropy_losses))
self.logger.record("train/policy_gradient_loss", np.mean(pg_losses))
self.logger.record("train/value_loss", np.mean(value_losses))
self.logger.record("train/approx_kl", np.mean(approx_kl_divs))
self.logger.record("train/clip_fraction", np.mean(clip_fractions))
self.logger.record("train/loss", loss.item())
self.logger.record("train/explained_variance", explained_var)
if hasattr(self.policy, "log_std"):
self.logger.record("train/std", th.exp(self.policy.log_std).mean().item())
self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
self.logger.record("train/clip_range", clip_range)
if self.clip_range_vf is not None:
self.logger.record("train/clip_range_vf", clip_range_vf)
def learn(
self,
total_timesteps: int,
callback: MaybeCallback = None,
log_interval: int = 1,
eval_env: Optional[GymEnv] = None,
eval_freq: int = -1,
n_eval_episodes: int = 5,
tb_log_name: str = "PPO",
eval_log_path: Optional[str] = None,
reset_num_timesteps: bool = True,
env_cfg: str = None,
) -> "PPO":
return super(PPO, self).learn(
total_timesteps=total_timesteps,
callback=callback,
log_interval=log_interval,
eval_env=eval_env,
eval_freq=eval_freq,
n_eval_episodes=n_eval_episodes,
tb_log_name=tb_log_name,
eval_log_path=eval_log_path,
reset_num_timesteps=reset_num_timesteps,
env_cfg=env_cfg,
)
def eval(self, iteration) -> None:
save_path = self.logger.get_dir() + "/TestTraj"
os.makedirs(save_path, exist_ok=True)
#
self.policy.eval()
self.eval_env.load_rms(
self.logger.get_dir() + "/RMS/iter_{0:05d}.npz".format(iteration)
)
# rollout trajectory and save the trajectory
traj_df = traj_rollout(self.eval_env, self.policy)
traj_df.to_csv(save_path + "/test_traj_{0:05d}.csv".format(iteration))
# generate plots
fig1 = plt.figure(figsize=(10, 6))
# fig1.subplots_adjust(
# left=None, bottom=None, right=None, top=None, wspace=None, hspace=None
# )
gs1 = gridspec.GridSpec(4, 3)
ax3d = fig1.add_subplot(gs1[2:3, 0:3], projection="3d")
axpos, axvel = [], []
for i in range(3):
axpos.append(fig1.add_subplot(gs1[0, i]))
axvel.append(fig1.add_subplot(gs1[1, i]))
episode_idx = traj_df.episode_id.unique()
for ep_i in episode_idx:
conditions = "episode_id == {0}".format(ep_i)
traj_episode_i = traj_df.query(conditions)
pos = traj_episode_i.loc[:, ["px", "py", "pz"]].to_numpy(dtype=np.float64)
vel = traj_episode_i.loc[:, ["vx", "vy", "vz"]].to_numpy(dtype=np.float64)
#
axpos[0].plot(pos[:, 0])
axpos[1].plot(pos[:, 1])
axpos[2].plot(pos[:, 2])
#
axvel[0].plot(vel[:, 0])
axvel[1].plot(vel[:, 1])
axvel[2].plot(vel[:, 2])
plot3d_traj(ax3d=ax3d, pos=pos, vel=vel)
#
save_path = self.logger.get_dir() + "/TestTraj" + "/Plots"
os.makedirs(save_path, exist_ok=True)
fig1.savefig(save_path + "/traj_3d_{0:05d}.png".format(iteration))