ray/rllib/agents/sac/sac.py

"""
Soft Actor Critic (SAC)
=======================

This file defines the distributed Trainer class for the soft actor critic
algorithm.
See `sac_[tf|torch]_policy.py` for the definition of the policy loss.

Detailed documentation: https://docs.ray.io/en/master/rllib-algorithms.html#sac
"""

import logging
from typing import Optional, Type

from ray.rllib.agents.trainer import with_common_config
from ray.rllib.agents.dqn.dqn import GenericOffPolicyTrainer
from ray.rllib.agents.sac.sac_tf_policy import SACTFPolicy
from ray.rllib.policy.policy import Policy
from ray.rllib.utils.deprecation import DEPRECATED_VALUE, deprecation_warning
from ray.rllib.utils.typing import TrainerConfigDict

logger = logging.getLogger(__name__)

OPTIMIZER_SHARED_CONFIGS = [
    "buffer_size", "prioritized_replay", "prioritized_replay_alpha",
    "prioritized_replay_beta", "prioritized_replay_eps",
    "rollout_fragment_length", "train_batch_size", "learning_starts"
]

# yapf: disable
# __sphinx_doc_begin__

# Adds the following updates to the (base) `Trainer` config in
# rllib/agents/trainer.py (`COMMON_CONFIG` dict).
DEFAULT_CONFIG = with_common_config({
    # === Model ===
    # Use two Q-networks (instead of one) for action-value estimation.
    # Note: Each Q-network will have its own target network.
    "twin_q": True,
    # Use a e.g. conv2D state preprocessing network before concatenating the
    # resulting (feature) vector with the action input for the input to
    # the Q-networks.
    "use_state_preprocessor": DEPRECATED_VALUE,
    # Model options for the Q network(s). These will override MODEL_DEFAULTS.
    # The `Q_model` dict is treated just as the top-level `model` dict in
    # setting up the Q-network(s) (2 if twin_q=True).
    # That means, you can do for different observation spaces:
    # obs=Box(1D) -> Tuple(Box(1D) + Action) -> concat -> post_fcnet
    # obs=Box(3D) -> Tuple(Box(3D) + Action) -> vision-net -> concat w/ action
    #   -> post_fcnet
    # obs=Tuple(Box(1D), Box(3D)) -> Tuple(Box(1D), Box(3D), Action)
    #   -> vision-net -> concat w/ Box(1D) and action -> post_fcnet
    # You can also have SAC use your custom_model as Q-model(s), by simply
    # specifying the `custom_model` sub-key in below dict (just like you would
    # do in the top-level `model` dict.
    "Q_model": {
        "fcnet_hiddens": [256, 256],
        "fcnet_activation": "relu",
        "post_fcnet_hiddens": [],
        "post_fcnet_activation": None,
        "custom_model": None,  # Use this to define custom Q-model(s).
        "custom_model_config": {},
    },
    # Model options for the policy function (see `Q_model` above for details).
    # The difference to `Q_model` above is that no action concat'ing is
    # performed before the post_fcnet stack.
    "policy_model": {
        "fcnet_hiddens": [256, 256],
        "fcnet_activation": "relu",
        "post_fcnet_hiddens": [],
        "post_fcnet_activation": None,
        "custom_model": None,  # Use this to define a custom policy model.
        "custom_model_config": {},
    },
    # Unsquash actions to the upper and lower bounds of env's action space.
    # Ignored for discrete action spaces.
    "normalize_actions": True,

    # === Learning ===
    # Disable setting done=True at end of episode. This should be set to True
    # for infinite-horizon MDPs (e.g., many continuous control problems).
    "no_done_at_end": False,
    # Update the target by \tau * policy + (1-\tau) * target_policy.
    "tau": 5e-3,
    # Initial value to use for the entropy weight alpha.
    "initial_alpha": 1.0,
    # Target entropy lower bound. If "auto", will be set to -|A| (e.g. -2.0 for
    # Discrete(2), -3.0 for Box(shape=(3,))).
    # This is the inverse of reward scale, and will be optimized automatically.
    "target_entropy": None,
    # N-step target updates. If >1, sars' tuples in trajectories will be
    # postprocessed to become sa[discounted sum of R][s t+n] tuples.
    "n_step": 1,
    # Number of env steps to optimize for before returning.
    "timesteps_per_iteration": 100,

    # === Replay buffer ===
    # Size of the replay buffer (in time steps).
    "buffer_size": int(1e6),
    # If True prioritized replay buffer will be used.
    "prioritized_replay": False,
    "prioritized_replay_alpha": 0.6,
    "prioritized_replay_beta": 0.4,
    "prioritized_replay_eps": 1e-6,
    "prioritized_replay_beta_annealing_timesteps": 20000,
    "final_prioritized_replay_beta": 0.4,
    # Whether to LZ4 compress observations
    "compress_observations": False,
    # If set, this will fix the ratio of replayed from a buffer and learned on
    # timesteps to sampled from an environment and stored in the replay buffer
    # timesteps. Otherwise, the replay will proceed at the native ratio
    # determined by (train_batch_size / rollout_fragment_length).
    "training_intensity": None,

    # === Optimization ===
    "optimization": {
        "actor_learning_rate": 3e-4,
        "critic_learning_rate": 3e-4,
        "entropy_learning_rate": 3e-4,
    },
    # If not None, clip gradients during optimization at this value.
    "grad_clip": None,
    # How many steps of the model to sample before learning starts.
    "learning_starts": 1500,
    # Update the replay buffer with this many samples at once. Note that this
    # setting applies per-worker if num_workers > 1.
    "rollout_fragment_length": 1,
    # Size of a batched sampled from replay buffer for training.
    "train_batch_size": 256,
    # Update the target network every `target_network_update_freq` steps.
    "target_network_update_freq": 0,

    # === Parallelism ===
    # Whether to use a GPU for local optimization.
    "num_gpus": 0,
    # Number of workers for collecting samples with. This only makes sense
    # to increase if your environment is particularly slow to sample, or if
    # you"re using the Async or Ape-X optimizers.
    "num_workers": 0,
    # Whether to allocate GPUs for workers (if > 0).
    "num_gpus_per_worker": 0,
    # Whether to allocate CPUs for workers (if > 0).
    "num_cpus_per_worker": 1,
    # Whether to compute priorities on workers.
    "worker_side_prioritization": False,
    # Prevent iterations from going lower than this time span.
    "min_iter_time_s": 1,

    # Whether the loss should be calculated deterministically (w/o the
    # stochastic action sampling step). True only useful for cont. actions and
    # for debugging!
    "_deterministic_loss": False,
    # Use a Beta-distribution instead of a SquashedGaussian for bounded,
    # continuous action spaces (not recommended, for debugging only).
    "_use_beta_distribution": False,
})
# __sphinx_doc_end__
# yapf: enable


def validate_config(config: TrainerConfigDict) -> None:
    """Validates the Trainer's config dict.

    Args:
        config (TrainerConfigDict): The Trainer's config to check.

    Raises:
        ValueError: In case something is wrong with the config.
    """
    if config["num_gpus"] > 1 and config["framework"] != "torch":
        raise ValueError("`num_gpus` > 1 not yet supported for tf-SAC!")

    if config["use_state_preprocessor"] != DEPRECATED_VALUE:
        deprecation_warning(
            old="config['use_state_preprocessor']", error=False)
        config["use_state_preprocessor"] = DEPRECATED_VALUE

    if config["grad_clip"] is not None and config["grad_clip"] <= 0.0:
        raise ValueError("`grad_clip` value must be > 0.0!")

    if config["simple_optimizer"] != DEPRECATED_VALUE or \
            config["simple_optimizer"] is False:
        logger.warning("`simple_optimizer` must be True (or unset) for SAC!")
        config["simple_optimizer"] = True


def get_policy_class(config: TrainerConfigDict) -> Optional[Type[Policy]]:
    """Policy class picker function. Class is chosen based on DL-framework.

    Args:
        config (TrainerConfigDict): The trainer's configuration dict.

    Returns:
        Optional[Type[Policy]]: The Policy class to use with PPOTrainer.
            If None, use `default_policy` provided in build_trainer().
    """
    if config["framework"] == "torch":
        from ray.rllib.agents.sac.sac_torch_policy import SACTorchPolicy
        return SACTorchPolicy


# Build a child class of `Trainer` (based on the kwargs used to create the
# GenericOffPolicyTrainer class and the kwargs used in the call below), which
# uses the framework specific Policy determined in `get_policy_class()` above.
SACTrainer = GenericOffPolicyTrainer.with_updates(
    name="SAC",
    default_config=DEFAULT_CONFIG,
    validate_config=validate_config,
    default_policy=SACTFPolicy,
    get_policy_class=get_policy_class,
)