[Trainers] SAC Trainer and algorithms

docs/source/reference/trainers.rst

@@ -183,6 +183,7 @@ Trainer and hooks
     Trainer
     TrainerHookBase
     UpdateWeights
+    TargetNetUpdaterHook
 
 
 Algorithm-specific trainers (Experimental)
@@ -202,37 +203,54 @@ into complete training solutions with sensible defaults and comprehensive config
     :template: rl_template.rst
 
     PPOTrainer
+    SACTrainer
 
-PPOTrainer
-~~~~~~~~~~
+Algorithm Trainers
+~~~~~~~~~~~~~~~~~~
 
-The :class:`~torchrl.trainers.algorithms.PPOTrainer` provides a complete PPO training solution
-with configurable defaults and a comprehensive configuration system built on Hydra.
+TorchRL provides high-level algorithm trainers that offer complete training solutions with minimal code.
+These trainers feature comprehensive configuration systems built on Hydra, enabling both simple usage 
+and sophisticated customization.
+
+**Currently Available:**
+
+- :class:`~torchrl.trainers.algorithms.PPOTrainer` - Proximal Policy Optimization
+- :class:`~torchrl.trainers.algorithms.SACTrainer` - Soft Actor-Critic
 
 **Key Features:**
 
-- Complete training pipeline with environment setup, data collection, and optimization
-- Extensive configuration system using dataclasses and Hydra
-- Built-in logging for rewards, actions, and training statistics  
-- Modular design built on existing TorchRL components
-- **Minimal code**: Complete SOTA implementation in just ~20 lines!
+- **Complete pipeline**: Environment setup, data collection, and optimization
+- **Hydra configuration**: Extensive dataclass-based configuration system
+- **Built-in logging**: Rewards, actions, and algorithm-specific metrics
+- **Modular design**: Built on existing TorchRL components
+- **Minimal code**: Complete SOTA implementations in ~20 lines!
 
 .. warning::
-    This is an experimental feature. The API may change in future versions. 
-    We welcome feedback and contributions to help improve this implementation!
+    Algorithm trainers are experimental features. The API may change in future versions. 
+    We welcome feedback and contributions to help improve these implementations!
 
-**Quick Start - Command Line Interface:**
+Quick Start Examples
+^^^^^^^^^^^^^^^^^^^^
+
+**PPO Training:**
 
 .. code-block:: bash
 
-    # Basic usage - train PPO on Pendulum-v1 with default settings
+    # Train PPO on Pendulum-v1 with default settings
     python sota-implementations/ppo_trainer/train.py
 
+**SAC Training:**
+
+.. code-block:: bash
+
+    # Train SAC on a continuous control task
+    python sota-implementations/sac_trainer/train.py
+
 **Custom Configuration:**
 
 .. code-block:: bash
 
-    # Override specific parameters via command line
+    # Override parameters for any algorithm
     python sota-implementations/ppo_trainer/train.py \
         trainer.total_frames=2000000 \
         training_env.create_env_fn.base_env.env_name=HalfCheetah-v4 \
@@ -243,32 +261,34 @@ with configurable defaults and a comprehensive configuration system built on Hyd
 
 .. code-block:: bash
 
-    # Switch to a different environment and logger
-    python sota-implementations/ppo_trainer/train.py \
-        env=gym \
+    # Switch environment and logger for any trainer
+    python sota-implementations/sac_trainer/train.py \
         training_env.create_env_fn.base_env.env_name=Walker2d-v4 \
-        logger=tensorboard
+        logger=tensorboard \
+        logger.exp_name=sac_walker2d
 
-**See All Options:**
+**View Configuration Options:**
 
 .. code-block:: bash
 
-    # View all available configuration options
+    # See all available options for any trainer
     python sota-implementations/ppo_trainer/train.py --help
+    python sota-implementations/sac_trainer/train.py --help
 
-**Configuration Groups:**
+Universal Configuration System
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The PPOTrainer configuration is organized into logical groups:
+All algorithm trainers share a unified configuration architecture organized into logical groups:
 
-- **Environment**: ``env_cfg__env_name``, ``env_cfg__backend``, ``env_cfg__device``
-- **Networks**: ``actor_network__network__num_cells``, ``critic_network__module__num_cells``
-- **Training**: ``total_frames``, ``clip_norm``, ``num_epochs``, ``optimizer_cfg__lr``
-- **Logging**: ``log_rewards``, ``log_actions``, ``log_observations``
+- **Environment**: ``training_env.create_env_fn.base_env.env_name``, ``training_env.num_workers``
+- **Networks**: ``networks.policy_network.num_cells``, ``networks.value_network.num_cells``
+- **Training**: ``trainer.total_frames``, ``trainer.clip_norm``, ``optimizer.lr``
+- **Data**: ``collector.frames_per_batch``, ``replay_buffer.batch_size``, ``replay_buffer.storage.max_size``
+- **Logging**: ``logger.exp_name``, ``logger.project``, ``trainer.log_interval``
 
 **Working Example:**
 
-The `sota-implementations/ppo_trainer/ <https://github.com/pytorch/rl/tree/main/sota-implementations/ppo_trainer>`_ 
-directory contains a complete, working PPO implementation that demonstrates the simplicity and power of the trainer system:
+All trainer implementations follow the same simple pattern:
 
 .. code-block:: python
 
@@ -283,33 +303,57 @@ directory contains a complete, working PPO implementation that demonstrates the
     if __name__ == "__main__":
         main()
 
-*Complete PPO training with full configurability in ~20 lines!*
+*Complete algorithm training with full configurability in ~20 lines!*
 
-**Configuration Classes:**
+Configuration Classes
+^^^^^^^^^^^^^^^^^^^^^
 
-The PPOTrainer uses a hierarchical configuration system with these main config classes.
+The trainer system uses a hierarchical configuration system with shared components.
 
 .. note::
    The configuration system requires Python 3.10+ due to its use of modern type annotation syntax.
 
-- **Trainer**: :class:`~torchrl.trainers.algorithms.configs.trainers.PPOTrainerConfig`
+**Algorithm-Specific Trainers:**
+
+- **PPO**: :class:`~torchrl.trainers.algorithms.configs.trainers.PPOTrainerConfig`
+- **SAC**: :class:`~torchrl.trainers.algorithms.configs.trainers.SACTrainerConfig`
+
+**Shared Configuration Components:**
+
 - **Environment**: :class:`~torchrl.trainers.algorithms.configs.envs_libs.GymEnvConfig`, :class:`~torchrl.trainers.algorithms.configs.envs.BatchedEnvConfig`
 - **Networks**: :class:`~torchrl.trainers.algorithms.configs.modules.MLPConfig`, :class:`~torchrl.trainers.algorithms.configs.modules.TanhNormalModelConfig`
 - **Data**: :class:`~torchrl.trainers.algorithms.configs.data.TensorDictReplayBufferConfig`, :class:`~torchrl.trainers.algorithms.configs.collectors.MultiaSyncDataCollectorConfig`
-- **Objectives**: :class:`~torchrl.trainers.algorithms.configs.objectives.PPOLossConfig`
+- **Objectives**: :class:`~torchrl.trainers.algorithms.configs.objectives.PPOLossConfig`, :class:`~torchrl.trainers.algorithms.configs.objectives.SACLossConfig`
 - **Optimizers**: :class:`~torchrl.trainers.algorithms.configs.utils.AdamConfig`, :class:`~torchrl.trainers.algorithms.configs.utils.AdamWConfig`
 - **Logging**: :class:`~torchrl.trainers.algorithms.configs.logging.WandbLoggerConfig`, :class:`~torchrl.trainers.algorithms.configs.logging.TensorboardLoggerConfig`
 
+Algorithm-Specific Features
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+**PPOTrainer:**
+
+- On-policy learning with advantage estimation
+- Policy clipping and value function optimization
+- Configurable number of epochs per batch
+- Built-in GAE (Generalized Advantage Estimation)
+
+**SACTrainer:**
+
+- Off-policy learning with replay buffer
+- Entropy-regularized policy optimization
+- Target network soft updates
+- Continuous action space optimization
+
 **Future Development:**
 
-This is the first of many planned algorithm-specific trainers. Future releases will include:
+The trainer system is actively expanding. Upcoming features include:
 
-- Additional algorithms: SAC, TD3, DQN, A2C, and more
-- Full integration of all TorchRL components within the configuration system
-- Enhanced configuration validation and error reporting
-- Distributed training support for high-level trainers
+- Additional algorithms: TD3, DQN, A2C, DDPG, and more
+- Enhanced distributed training support
+- Advanced configuration validation and error reporting
+- Integration with more TorchRL ecosystem components
 
-See the complete `configuration system documentation <https://github.com/pytorch/rl/tree/main/torchrl/trainers/algorithms/configs>`_ for all available options.
+See the complete `configuration system documentation <https://github.com/pytorch/rl/tree/main/torchrl/trainers/algorithms/configs>`_ for all available options and examples.
 
 
 Builders

sota-implementations/sac_trainer/config/config.yaml

@@ -0,0 +1,146 @@
+# SAC Trainer Configuration for HalfCheetah-v4
+# This configuration uses the new configurable trainer system and matches SOTA SAC implementation
+
+defaults:
+
+  - transform@transform0: step_counter
+  - transform@transform1: double_to_float
+
+  - env@training_env: batched_env
+  - env@training_env.create_env_fn: transformed_env
+  - env@training_env.create_env_fn.base_env: gym
+  - transform@training_env.create_env_fn.transform: compose
+
+  - model@models.policy_model: tanh_normal
+  - model@models.value_model: value
+  - model@models.qvalue_model: value
+
+  - network@networks.policy_network: mlp
+  - network@networks.value_network: mlp
+  - network@networks.qvalue_network: mlp
+
+  - collector@collector: multi_async
+
+  - replay_buffer@replay_buffer: base
+  - storage@replay_buffer.storage: lazy_tensor
+  - writer@replay_buffer.writer: round_robin
+  - sampler@replay_buffer.sampler: random
+  - trainer@trainer: sac
+  - optimizer@optimizer: adam
+  - loss@loss: sac
+  - target_net_updater@target_net_updater: soft
+  - logger@logger: wandb
+  - _self_
+
+# Network configurations
+networks:
+  policy_network:
+    out_features: 12  # HalfCheetah action space is 6-dimensional (loc + scale)
+    in_features: 17   # HalfCheetah observation space is 17-dimensional
+    num_cells: [256, 256]
+
+  value_network:
+    out_features: 1  # Value output
+    in_features: 17   # HalfCheetah observation space
+    num_cells: [256, 256]
+
+  qvalue_network:
+    out_features: 1  # Q-value output
+    in_features: 23   # HalfCheetah observation space (17) + action space (6)
+    num_cells: [256, 256]
+
+# Model configurations
+models:
+  policy_model:
+    return_log_prob: true
+    in_keys: ["observation"]
+    param_keys: ["loc", "scale"]
+    out_keys: ["action"]
+    network: ${networks.policy_network}
+
+  qvalue_model:
+    in_keys: ["observation", "action"]
+    out_keys: ["state_action_value"]
+    network: ${networks.qvalue_network}
+
+transform0:
+  max_steps: 1000
+  step_count_key: "step_count"
+
+transform1:
+  # DoubleToFloatTransform - converts double precision to float to fix dtype mismatch
+  in_keys: null
+  out_keys: null
+
+training_env:
+  num_workers: 4
+  create_env_fn:
+    base_env:
+      env_name: HalfCheetah-v4
+    transform:
+      transforms:
+        - ${transform0}
+        - ${transform1}
+    _partial_: true
+
+# Loss configuration
+loss:
+  actor_network: ${models.policy_model}
+  qvalue_network: ${models.qvalue_model}
+  target_entropy: "auto"
+  loss_function: l2
+  alpha_init: 1.0
+  delay_qvalue: true
+  num_qvalue_nets: 2
+
+target_net_updater:
+  tau: 0.001
+
+# Optimizer configuration
+optimizer:
+  lr: 3.0e-4
+
+# Collector configuration
+collector:
+  create_env_fn: ${training_env}
+  policy: ${models.policy_model}
+  total_frames: 1_000_000
+  frames_per_batch: 1000
+  num_workers: 4
+  init_random_frames: 25000
+  track_policy_version: true
+
+# Replay buffer configuration
+replay_buffer:
+  storage:
+    max_size: 1_000_000
+    device: cpu
+    ndim: 1
+  sampler:
+  writer:
+    compilable: false
+  batch_size: 256
+
+logger:
+  exp_name: sac_halfcheetah_v4
+  offline: false
+  project: torchrl-sota-implementations
+
+# Trainer configuration
+trainer:
+  collector: ${collector}
+  optimizer: ${optimizer}
+  replay_buffer: ${replay_buffer}
+  target_net_updater: ${target_net_updater}
+  loss_module: ${loss}
+  logger: ${logger}
+  total_frames: 1_000_000
+  frame_skip: 1
+  clip_grad_norm: false  # SAC typically doesn't use gradient clipping
+  clip_norm: null
+  progress_bar: true
+  seed: 42
+  save_trainer_interval: 25000  # Match SOTA eval_iter
+  log_interval: 25000
+  save_trainer_file: null
+  optim_steps_per_batch: 64  # Match SOTA utd_ratio

sota-implementations/sac_trainer/train.py

@@ -0,0 +1,21 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import hydra
+import torchrl
+from torchrl.trainers.algorithms.configs import *  # noqa: F401, F403
+
+
+@hydra.main(config_path="config", config_name="config", version_base="1.1")
+def main(cfg):
+    def print_reward(td):
+        torchrl.logger.info(f"reward: {td['next', 'reward'].mean(): 4.4f}")
+
+    trainer = hydra.utils.instantiate(cfg.trainer)
+    trainer.register_op(dest="batch_process", op=print_reward)
+    trainer.train()
+
+
+if __name__ == "__main__":
+    main()

torchrl/trainers/__init__.py

@@ -16,6 +16,7 @@
     ReplayBufferTrainer,
     RewardNormalizer,
     SelectKeys,
+    TargetNetUpdaterHook,
     Trainer,
     TrainerHookBase,
     UpdateWeights,
@@ -37,4 +38,5 @@
     "Trainer",
     "TrainerHookBase",
     "UpdateWeights",
+    "TargetNetUpdaterHook",
 ]

torchrl/trainers/algorithms/__init__.py

@@ -6,5 +6,6 @@
 from __future__ import annotations
 
 from .ppo import PPOTrainer
+from .sac import SACTrainer
 
-__all__ = ["PPOTrainer"]
+__all__ = ["PPOTrainer", "SACTrainer"]