mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-06-05 00:00:04 +00:00
216 lines
9.4 KiB
Python
216 lines
9.4 KiB
Python
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
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import torch as th
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from torch.nn import functional as F
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from stable_baselines3.common import logger
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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 stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
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from stable_baselines3.common.utils import polyak_update
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from stable_baselines3.td3.policies import TD3Policy
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class TD3(OffPolicyAlgorithm):
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"""
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Twin Delayed DDPG (TD3)
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Addressing Function Approximation Error in Actor-Critic Methods.
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Original implementation: https://github.com/sfujim/TD3
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Paper: https://arxiv.org/abs/1802.09477
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Introduction to TD3: https://spinningup.openai.com/en/latest/algorithms/td3.html
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:param policy: (TD3Policy or str) The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: (GymEnv or str) The environment to learn from (if registered in Gym, can be str)
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:param learning_rate: (float or callable) 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: (int) size of the replay buffer
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:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
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:param batch_size: (int) Minibatch size for each gradient update
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:param tau: (float) the soft update coefficient ("Polyak update", between 0 and 1)
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:param gamma: (float) the discount factor
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:param train_freq: (int) Update the model every ``train_freq`` steps. Set to `-1` to disable.
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:param gradient_steps: (int) How many gradient steps to do after each rollout
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(see ``train_freq`` and ``n_episodes_rollout``)
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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 n_episodes_rollout: (int) Update the model every ``n_episodes_rollout`` episodes.
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Note that this cannot be used at the same time as ``train_freq``. Set to `-1` to disable.
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:param action_noise: (ActionNoise) 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 optimize_memory_usage: (bool) 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 policy_delay: (int) Policy and target networks will only be updated once every policy_delay steps
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per training steps. The Q values will be updated policy_delay more often (update every training step).
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:param target_policy_noise: (float) Standard deviation of Gaussian noise added to target policy
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(smoothing noise)
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:param target_noise_clip: (float) Limit for absolute value of target policy smoothing noise.
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:param create_eval_env: (bool) Whether to create a second environment that will be
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used for evaluating the agent periodically. (Only available when passing string for the environment)
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:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
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:param verbose: (int) the verbosity level: 0 no output, 1 info, 2 debug
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:param seed: (int) Seed for the pseudo random generators
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:param device: (str or th.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: (bool) Whether or not to build the network at the creation of the instance
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"""
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def __init__(
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self,
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policy: Union[str, Type[TD3Policy]],
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env: Union[GymEnv, str],
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learning_rate: Union[float, Callable] = 1e-3,
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buffer_size: int = int(1e6),
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learning_starts: int = 100,
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batch_size: int = 100,
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tau: float = 0.005,
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gamma: float = 0.99,
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train_freq: int = -1,
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gradient_steps: int = -1,
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n_episodes_rollout: int = 1,
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action_noise: Optional[ActionNoise] = None,
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optimize_memory_usage: bool = False,
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policy_delay: int = 2,
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target_policy_noise: float = 0.2,
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target_noise_clip: float = 0.5,
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tensorboard_log: Optional[str] = None,
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create_eval_env: bool = False,
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policy_kwargs: 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(TD3, self).__init__(
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policy,
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env,
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TD3Policy,
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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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n_episodes_rollout,
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action_noise=action_noise,
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policy_kwargs=policy_kwargs,
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tensorboard_log=tensorboard_log,
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verbose=verbose,
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device=device,
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create_eval_env=create_eval_env,
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seed=seed,
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sde_support=False,
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optimize_memory_usage=optimize_memory_usage,
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)
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self.policy_delay = policy_delay
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self.target_noise_clip = target_noise_clip
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self.target_policy_noise = target_policy_noise
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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(TD3, self)._setup_model()
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self._create_aliases()
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def _create_aliases(self) -> None:
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self.actor = self.policy.actor
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self.actor_target = self.policy.actor_target
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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 = 100) -> None:
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# Update learning rate according to lr schedule
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self._update_learning_rate([self.actor.optimizer, self.critic.optimizer])
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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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with th.no_grad():
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# Select action according to policy and add clipped noise
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noise = replay_data.actions.clone().data.normal_(0, self.target_policy_noise)
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noise = noise.clamp(-self.target_noise_clip, self.target_noise_clip)
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next_actions = (self.actor_target(replay_data.next_observations) + noise).clamp(-1, 1)
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# Compute the target Q value: min over all critics targets
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targets = th.cat(self.critic_target(replay_data.next_observations, next_actions), dim=1)
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target_q, _ = th.min(targets, dim=1, keepdim=True)
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target_q = replay_data.rewards + (1 - replay_data.dones) * self.gamma * target_q
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# Get current Q estimates for each critic network
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current_q_estimates = self.critic(replay_data.observations, replay_data.actions)
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# Compute critic loss
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critic_loss = sum([F.mse_loss(current_q, target_q) for current_q in current_q_estimates])
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# Optimize the critics
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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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# Delayed policy updates
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if gradient_step % self.policy_delay == 0:
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# Compute actor loss
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actor_loss = -self.critic.q1_forward(replay_data.observations, self.actor(replay_data.observations)).mean()
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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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polyak_update(self.critic.parameters(), self.critic_target.parameters(), self.tau)
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polyak_update(self.actor.parameters(), self.actor_target.parameters(), self.tau)
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self._n_updates += gradient_steps
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logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
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def learn(
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self,
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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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eval_env: Optional[GymEnv] = None,
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eval_freq: int = -1,
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n_eval_episodes: int = 5,
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tb_log_name: str = "TD3",
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eval_log_path: Optional[str] = None,
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reset_num_timesteps: bool = True,
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) -> OffPolicyAlgorithm:
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return super(TD3, self).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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eval_env=eval_env,
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eval_freq=eval_freq,
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n_eval_episodes=n_eval_episodes,
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tb_log_name=tb_log_name,
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eval_log_path=eval_log_path,
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reset_num_timesteps=reset_num_timesteps,
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)
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def excluded_save_params(self) -> List[str]:
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"""
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Returns the names of the parameters that should be excluded by default
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when saving the model.
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:return: (List[str]) List of parameters that should be excluded from save
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"""
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# Exclude aliases
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return super(TD3, self).excluded_save_params() + ["actor", "critic", "actor_target", "critic_target"]
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def get_torch_variables(self) -> Tuple[List[str], List[str]]:
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"""
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cf base class
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"""
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state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
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return state_dicts, []
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