from typing import Any, ClassVar, Optional, TypeVar, Union import numpy as np import torch as th from gymnasium import spaces from torch.nn import functional as F from stable_baselines3.common.buffers import ReplayBuffer from stable_baselines3.common.noise import ActionNoise from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm from stable_baselines3.common.policies import BasePolicy, ContinuousCritic from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule from stable_baselines3.common.utils import get_parameters_by_name, polyak_update from stable_baselines3.td3.policies import Actor, CnnPolicy, MlpPolicy, MultiInputPolicy, TD3Policy SelfTD3 = TypeVar("SelfTD3", bound="TD3") class TD3(OffPolicyAlgorithm): """ Twin Delayed DDPG (TD3) Addressing Function Approximation Error in Actor-Critic Methods. Original implementation: https://github.com/sfujim/TD3 Paper: https://arxiv.org/abs/1802.09477 Introduction to TD3: https://spinningup.openai.com/en/latest/algorithms/td3.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: learning rate for adam optimizer, the same learning rate will be used for all networks (Q-Values, Actor and Value function) it can be a function of the current progress remaining (from 1 to 0) :param buffer_size: size of the replay buffer :param learning_starts: how many steps of the model to collect transitions for before learning starts :param batch_size: Minibatch size for each gradient update :param tau: the soft update coefficient ("Polyak update", between 0 and 1) :param gamma: the discount factor :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit like ``(5, "step")`` or ``(2, "episode")``. :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``) Set to ``-1`` means to do as many gradient steps as steps done in the environment during the rollout. :param action_noise: the action noise type (None by default), this can help for hard exploration problem. Cf common.noise for the different action noise type. :param replay_buffer_class: Replay buffer class to use (for instance ``HerReplayBuffer``). If ``None``, it will be automatically selected. :param replay_buffer_kwargs: Keyword arguments to pass to the replay buffer on creation. :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer at a cost of more complexity. See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195 :param n_steps: When n_step > 1, uses n-step return (with the NStepReplayBuffer) when updating the Q-value network. :param policy_delay: Policy and target networks will only be updated once every policy_delay steps per training steps. The Q values will be updated policy_delay more often (update every training step). :param target_policy_noise: Standard deviation of Gaussian noise added to target policy (smoothing noise) :param target_noise_clip: Limit for absolute value of target policy smoothing noise. :param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average the reported success rate, mean episode length, and mean reward over :param tensorboard_log: the log location for tensorboard (if None, no logging) :param policy_kwargs: additional arguments to be passed to the policy on creation. See :ref:`td3_policies` :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for debug messages :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 """ policy_aliases: ClassVar[dict[str, type[BasePolicy]]] = { "MlpPolicy": MlpPolicy, "CnnPolicy": CnnPolicy, "MultiInputPolicy": MultiInputPolicy, } policy: TD3Policy actor: Actor actor_target: Actor critic: ContinuousCritic critic_target: ContinuousCritic def __init__( self, policy: Union[str, type[TD3Policy]], env: Union[GymEnv, str], learning_rate: Union[float, Schedule] = 1e-3, buffer_size: int = 1_000_000, # 1e6 learning_starts: int = 100, batch_size: int = 256, tau: float = 0.005, gamma: float = 0.99, train_freq: Union[int, tuple[int, str]] = 1, gradient_steps: int = 1, action_noise: Optional[ActionNoise] = None, replay_buffer_class: Optional[type[ReplayBuffer]] = None, replay_buffer_kwargs: Optional[dict[str, Any]] = None, optimize_memory_usage: bool = False, n_steps: int = 1, policy_delay: int = 2, target_policy_noise: float = 0.2, target_noise_clip: float = 0.5, stats_window_size: int = 100, tensorboard_log: Optional[str] = None, policy_kwargs: Optional[dict[str, Any]] = None, verbose: int = 0, seed: Optional[int] = None, device: Union[th.device, str] = "auto", _init_setup_model: bool = True, ): super().__init__( policy, env, learning_rate, buffer_size, learning_starts, batch_size, tau, gamma, train_freq, gradient_steps, action_noise=action_noise, replay_buffer_class=replay_buffer_class, replay_buffer_kwargs=replay_buffer_kwargs, optimize_memory_usage=optimize_memory_usage, n_steps=n_steps, policy_kwargs=policy_kwargs, stats_window_size=stats_window_size, tensorboard_log=tensorboard_log, verbose=verbose, device=device, seed=seed, sde_support=False, supported_action_spaces=(spaces.Box,), support_multi_env=True, ) self.policy_delay = policy_delay self.target_noise_clip = target_noise_clip self.target_policy_noise = target_policy_noise if _init_setup_model: self._setup_model() def _setup_model(self) -> None: super()._setup_model() self._create_aliases() # Running mean and running var self.actor_batch_norm_stats = get_parameters_by_name(self.actor, ["running_"]) self.critic_batch_norm_stats = get_parameters_by_name(self.critic, ["running_"]) self.actor_batch_norm_stats_target = get_parameters_by_name(self.actor_target, ["running_"]) self.critic_batch_norm_stats_target = get_parameters_by_name(self.critic_target, ["running_"]) def _create_aliases(self) -> None: self.actor = self.policy.actor self.actor_target = self.policy.actor_target self.critic = self.policy.critic self.critic_target = self.policy.critic_target def train(self, gradient_steps: int, batch_size: int = 100) -> None: # Switch to train mode (this affects batch norm / dropout) self.policy.set_training_mode(True) # Update learning rate according to lr schedule self._update_learning_rate([self.actor.optimizer, self.critic.optimizer]) actor_losses, critic_losses = [], [] for _ in range(gradient_steps): self._n_updates += 1 # Sample replay buffer replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env) # type: ignore[union-attr] # For n-step replay, discount factor is gamma**n_steps (when no early termination) discounts = replay_data.discounts if replay_data.discounts is not None else self.gamma with th.no_grad(): # Select action according to policy and add clipped noise noise = replay_data.actions.clone().data.normal_(0, self.target_policy_noise) noise = noise.clamp(-self.target_noise_clip, self.target_noise_clip) next_actions = (self.actor_target(replay_data.next_observations) + noise).clamp(-1, 1) # Compute the next Q-values: min over all critics targets next_q_values = th.cat(self.critic_target(replay_data.next_observations, next_actions), dim=1) next_q_values, _ = th.min(next_q_values, dim=1, keepdim=True) target_q_values = replay_data.rewards + (1 - replay_data.dones) * discounts * next_q_values # Get current Q-values estimates for each critic network current_q_values = self.critic(replay_data.observations, replay_data.actions) # Compute critic loss critic_loss = sum(F.mse_loss(current_q, target_q_values) for current_q in current_q_values) assert isinstance(critic_loss, th.Tensor) critic_losses.append(critic_loss.item()) # Optimize the critics self.critic.optimizer.zero_grad() critic_loss.backward() self.critic.optimizer.step() # Delayed policy updates if self._n_updates % self.policy_delay == 0: # Compute actor loss actor_loss = -self.critic.q1_forward(replay_data.observations, self.actor(replay_data.observations)).mean() actor_losses.append(actor_loss.item()) # Optimize the actor self.actor.optimizer.zero_grad() actor_loss.backward() self.actor.optimizer.step() polyak_update(self.critic.parameters(), self.critic_target.parameters(), self.tau) polyak_update(self.actor.parameters(), self.actor_target.parameters(), self.tau) # Copy running stats, see GH issue #996 polyak_update(self.critic_batch_norm_stats, self.critic_batch_norm_stats_target, 1.0) polyak_update(self.actor_batch_norm_stats, self.actor_batch_norm_stats_target, 1.0) self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard") if len(actor_losses) > 0: self.logger.record("train/actor_loss", np.mean(actor_losses)) self.logger.record("train/critic_loss", np.mean(critic_losses)) def learn( self: SelfTD3, total_timesteps: int, callback: MaybeCallback = None, log_interval: int = 4, tb_log_name: str = "TD3", reset_num_timesteps: bool = True, progress_bar: bool = False, ) -> SelfTD3: return super().learn( total_timesteps=total_timesteps, callback=callback, log_interval=log_interval, tb_log_name=tb_log_name, reset_num_timesteps=reset_num_timesteps, progress_bar=progress_bar, ) def _excluded_save_params(self) -> list[str]: return super()._excluded_save_params() + ["actor", "critic", "actor_target", "critic_target"] # noqa: RUF005 def _get_torch_save_params(self) -> tuple[list[str], list[str]]: state_dicts = ["policy", "actor.optimizer", "critic.optimizer"] return state_dicts, []