327 lines
15 KiB
Python
327 lines
15 KiB
Python
from typing import Any, Dict, List, Optional, Tuple, Type, TypeVar, Union
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import numpy as np
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import torch as th
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from gym import spaces
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from torch.nn import functional as F
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from stable_baselines3.common.buffers import ReplayBuffer
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from stable_baselines3.common.noise import ActionNoise
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# from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
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from common.off_policy_algorithm import BetterOffPolicyAlgorithm
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from stable_baselines3.common.policies import BasePolicy
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from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
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from stable_baselines3.common.utils import get_parameters_by_name, polyak_update
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from stable_baselines3.sac.policies import CnnPolicy, MlpPolicy, MultiInputPolicy, SACPolicy
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SelfSAC = TypeVar("SelfSAC", bound="SAC")
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class SAC(BetterOffPolicyAlgorithm):
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"""
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Soft Actor-Critic (SAC)
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Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
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This implementation borrows code from original implementation (https://github.com/haarnoja/sac)
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from OpenAI Spinning Up (https://github.com/openai/spinningup), from the softlearning repo
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(https://github.com/rail-berkeley/softlearning/)
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and from Stable Baselines (https://github.com/hill-a/stable-baselines)
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Paper: https://arxiv.org/abs/1801.01290
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Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html
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Note: we use double q target and not value target as discussed
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in https://github.com/hill-a/stable-baselines/issues/270
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:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: The environment to learn from (if registered in Gym, can be str)
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:param learning_rate: learning rate for adam optimizer,
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the same learning rate will be used for all networks (Q-Values, Actor and Value function)
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it can be a function of the current progress remaining (from 1 to 0)
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:param buffer_size: size of the replay buffer
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:param learning_starts: how many steps of the model to collect transitions for before learning starts
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:param batch_size: Minibatch size for each gradient update
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:param tau: the soft update coefficient ("Polyak update", between 0 and 1)
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:param gamma: the discount factor
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:param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
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like ``(5, "step")`` or ``(2, "episode")``.
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:param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
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Set to ``-1`` means to do as many gradient steps as steps done in the environment
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during the rollout.
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:param action_noise: the action noise type (None by default), this can help
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for hard exploration problem. Cf common.noise for the different action noise type.
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:param replay_buffer_class: Replay buffer class to use (for instance ``HerReplayBuffer``).
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If ``None``, it will be automatically selected.
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:param replay_buffer_kwargs: Keyword arguments to pass to the replay buffer on creation.
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:param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
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at a cost of more complexity.
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See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
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:param ent_coef: Entropy regularization coefficient. (Equivalent to
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inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.
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Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
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:param target_update_interval: update the target network every ``target_network_update_freq``
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gradient steps.
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:param target_entropy: target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
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:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
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instead of action noise exploration (default: False)
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:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
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Default: -1 (only sample at the beginning of the rollout)
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:param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
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during the warm up phase (before learning starts)
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:param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
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the reported success rate, mean episode length, and mean reward over
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:param tensorboard_log: the log location for tensorboard (if None, no logging)
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:param policy_kwargs: additional arguments to be passed to the policy on creation
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:param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
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debug messages
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:param seed: Seed for the pseudo random generators
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:param device: Device (cpu, cuda, ...) on which the code should be run.
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Setting it to auto, the code will be run on the GPU if possible.
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:param _init_setup_model: Whether or not to build the network at the creation of the instance
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"""
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policy_aliases: Dict[str, Type[BasePolicy]] = {
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"MlpPolicy": MlpPolicy,
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"CnnPolicy": CnnPolicy,
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"MultiInputPolicy": MultiInputPolicy,
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}
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def __init__(
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self,
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policy: Union[str, Type[SACPolicy]],
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env: Union[GymEnv, str],
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learning_rate: Union[float, Schedule] = 3e-4,
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buffer_size: int = 1_000_000, # 1e6
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learning_starts: int = 100,
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batch_size: int = 256,
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tau: float = 0.005,
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gamma: float = 0.99,
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train_freq: Union[int, Tuple[int, str]] = 1,
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gradient_steps: int = 1,
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action_noise: Optional[ActionNoise] = None,
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replay_buffer_class: Optional[Type[ReplayBuffer]] = None,
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replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
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optimize_memory_usage: bool = False,
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ent_coef: Union[str, float] = "auto",
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target_update_interval: int = 1,
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target_entropy: Union[str, float] = "auto",
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use_sde: bool = False,
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sde_sample_freq: int = -1,
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use_sde_at_warmup: bool = False,
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use_pca: bool = False,
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stats_window_size: int = 100,
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tensorboard_log: Optional[str] = None,
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policy_kwargs: Optional[Dict[str, Any]] = None,
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verbose: int = 0,
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seed: Optional[int] = None,
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device: Union[th.device, str] = "auto",
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_init_setup_model: bool = True,
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):
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super().__init__(
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policy,
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env,
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learning_rate,
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buffer_size,
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learning_starts,
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batch_size,
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tau,
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gamma,
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train_freq,
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gradient_steps,
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action_noise,
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replay_buffer_class=replay_buffer_class,
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replay_buffer_kwargs=replay_buffer_kwargs,
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policy_kwargs=policy_kwargs,
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stats_window_size=stats_window_size,
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tensorboard_log=tensorboard_log,
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verbose=verbose,
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device=device,
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seed=seed,
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use_sde=use_sde,
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sde_sample_freq=sde_sample_freq,
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use_sde_at_warmup=use_sde_at_warmup,
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use_pca=use_pca,
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optimize_memory_usage=optimize_memory_usage,
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supported_action_spaces=(spaces.Box),
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support_multi_env=True,
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)
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print('[i] Using sbBrix version of SAC')
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self.target_entropy = target_entropy
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self.log_ent_coef = None # type: Optional[th.Tensor]
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# Entropy coefficient / Entropy temperature
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# Inverse of the reward scale
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self.ent_coef = ent_coef
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self.target_update_interval = target_update_interval
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self.ent_coef_optimizer = None
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if _init_setup_model:
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self._setup_model()
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def _setup_model(self) -> None:
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super()._setup_model()
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self._create_aliases()
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# Running mean and running var
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self.batch_norm_stats = get_parameters_by_name(self.critic, ["running_"])
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self.batch_norm_stats_target = get_parameters_by_name(self.critic_target, ["running_"])
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# Target entropy is used when learning the entropy coefficient
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if self.target_entropy == "auto":
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# automatically set target entropy if needed
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self.target_entropy = -np.prod(self.env.action_space.shape).astype(np.float32)
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else:
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# Force conversion
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# this will also throw an error for unexpected string
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self.target_entropy = float(self.target_entropy)
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# The entropy coefficient or entropy can be learned automatically
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# see Automating Entropy Adjustment for Maximum Entropy RL section
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# of https://arxiv.org/abs/1812.05905
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if isinstance(self.ent_coef, str) and self.ent_coef.startswith("auto"):
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# Default initial value of ent_coef when learned
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init_value = 1.0
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if "_" in self.ent_coef:
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init_value = float(self.ent_coef.split("_")[1])
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assert init_value > 0.0, "The initial value of ent_coef must be greater than 0"
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# Note: we optimize the log of the entropy coeff which is slightly different from the paper
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# as discussed in https://github.com/rail-berkeley/softlearning/issues/37
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self.log_ent_coef = th.log(th.ones(1, device=self.device) * init_value).requires_grad_(True)
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self.ent_coef_optimizer = th.optim.Adam([self.log_ent_coef], lr=self.lr_schedule(1))
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else:
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# Force conversion to float
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# this will throw an error if a malformed string (different from 'auto')
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# is passed
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self.ent_coef_tensor = th.tensor(float(self.ent_coef), device=self.device)
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def _create_aliases(self) -> None:
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self.actor = self.policy.actor
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self.critic = self.policy.critic
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self.critic_target = self.policy.critic_target
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def train(self, gradient_steps: int, batch_size: int = 64) -> None:
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# Switch to train mode (this affects batch norm / dropout)
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self.policy.set_training_mode(True)
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# Update optimizers learning rate
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optimizers = [self.actor.optimizer, self.critic.optimizer]
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if self.ent_coef_optimizer is not None:
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optimizers += [self.ent_coef_optimizer]
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# Update learning rate according to lr schedule
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self._update_learning_rate(optimizers)
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ent_coef_losses, ent_coefs = [], []
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actor_losses, critic_losses = [], []
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for gradient_step in range(gradient_steps):
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# Sample replay buffer
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replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
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# We need to sample because `log_std` may have changed between two gradient steps
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if self.use_sde:
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self.actor.reset_noise()
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# Action by the current actor for the sampled state
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actions_pi, log_prob = self.actor.action_log_prob(replay_data.observations)
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log_prob = log_prob.reshape(-1, 1)
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ent_coef_loss = None
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if self.ent_coef_optimizer is not None:
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# Important: detach the variable from the graph
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# so we don't change it with other losses
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# see https://github.com/rail-berkeley/softlearning/issues/60
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ent_coef = th.exp(self.log_ent_coef.detach())
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ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean()
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ent_coef_losses.append(ent_coef_loss.item())
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else:
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ent_coef = self.ent_coef_tensor
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ent_coefs.append(ent_coef.item())
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# Optimize entropy coefficient, also called
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# entropy temperature or alpha in the paper
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if ent_coef_loss is not None:
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self.ent_coef_optimizer.zero_grad()
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ent_coef_loss.backward()
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self.ent_coef_optimizer.step()
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with th.no_grad():
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# Select action according to policy
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next_actions, next_log_prob = self.actor.action_log_prob(replay_data.next_observations)
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# Compute the next Q values: min over all critics targets
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next_q_values = th.cat(self.critic_target(replay_data.next_observations, next_actions), dim=1)
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next_q_values, _ = th.min(next_q_values, dim=1, keepdim=True)
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# add entropy term
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next_q_values = next_q_values - ent_coef * next_log_prob.reshape(-1, 1)
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# td error + entropy term
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target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
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# Get current Q-values estimates for each critic network
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# using action from the replay buffer
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current_q_values = self.critic(replay_data.observations, replay_data.actions)
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# Compute critic loss
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critic_loss = 0.5 * sum(F.mse_loss(current_q, target_q_values) for current_q in current_q_values)
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critic_losses.append(critic_loss.item())
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# Optimize the critic
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self.critic.optimizer.zero_grad()
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critic_loss.backward()
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self.critic.optimizer.step()
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# Compute actor loss
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# Alternative: actor_loss = th.mean(log_prob - qf1_pi)
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# Min over all critic networks
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q_values_pi = th.cat(self.critic(replay_data.observations, actions_pi), dim=1)
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min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
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actor_loss = (ent_coef * log_prob - min_qf_pi).mean()
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actor_losses.append(actor_loss.item())
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# Optimize the actor
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self.actor.optimizer.zero_grad()
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actor_loss.backward()
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self.actor.optimizer.step()
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# Update target networks
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if gradient_step % self.target_update_interval == 0:
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polyak_update(self.critic.parameters(), self.critic_target.parameters(), self.tau)
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# Copy running stats, see GH issue #996
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polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)
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self._n_updates += gradient_steps
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self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
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self.logger.record("train/ent_coef", np.mean(ent_coefs))
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self.logger.record("train/actor_loss", np.mean(actor_losses))
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self.logger.record("train/critic_loss", np.mean(critic_losses))
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if len(ent_coef_losses) > 0:
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self.logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
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def learn(
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self: SelfSAC,
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total_timesteps: int,
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callback: MaybeCallback = None,
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log_interval: int = 4,
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tb_log_name: str = "SAC",
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reset_num_timesteps: bool = True,
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progress_bar: bool = False,
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) -> SelfSAC:
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return super().learn(
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total_timesteps=total_timesteps,
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callback=callback,
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log_interval=log_interval,
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tb_log_name=tb_log_name,
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reset_num_timesteps=reset_num_timesteps,
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progress_bar=progress_bar,
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)
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def _excluded_save_params(self) -> List[str]:
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return super()._excluded_save_params() + ["actor", "critic", "critic_target"] # noqa: RUF005
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def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
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state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
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if self.ent_coef_optimizer is not None:
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saved_pytorch_variables = ["log_ent_coef"]
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state_dicts.append("ent_coef_optimizer")
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else:
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saved_pytorch_variables = ["ent_coef_tensor"]
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return state_dicts, saved_pytorch_variables
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