stable-baselines3/stable_baselines3/td3/td3.py

import torch as th
import torch.nn.functional as F
from typing import List, Tuple, Type, Union, Callable, Optional, Dict, Any

from stable_baselines3.common import logger
from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
from stable_baselines3.common.noise import ActionNoise
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
from stable_baselines3.td3.policies import TD3Policy


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: (TD3Policy or str) The policy model to use (MlpPolicy, CnnPolicy, ...)
    :param env: (GymEnv or str) The environment to learn from (if registered in Gym, can be str)
    :param learning_rate: (float or callable) 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: (int) size of the replay buffer
    :param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
    :param batch_size: (int) Minibatch size for each gradient update
    :param tau: (float) the soft update coefficient ("Polyak update", between 0 and 1)
    :param gamma: (float) the discount factor
    :param train_freq: (int) Update the model every ``train_freq`` steps.
    :param gradient_steps: (int) How many gradient update after each step
    :param n_episodes_rollout: (int) Update the model every ``n_episodes_rollout`` episodes.
        Note that this cannot be used at the same time as ``train_freq``
    :param action_noise: (ActionNoise) the action noise type (None by default), this can help
        for hard exploration problem. Cf common.noise for the different action noise type.
    :param optimize_memory_usage: (bool) 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 policy_delay: (int) 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: (float) Standard deviation of Gaussian noise added to target policy
        (smoothing noise)
    :param target_noise_clip: (float) Limit for absolute value of target policy smoothing noise.
    :param create_eval_env: (bool) Whether to create a second environment that will be
        used for evaluating the agent periodically. (Only available when passing string for the environment)
    :param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
    :param verbose: (int) the verbosity level: 0 no output, 1 info, 2 debug
    :param seed: (int) Seed for the pseudo random generators
    :param device: (str or th.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: (bool) Whether or not to build the network at the creation of the instance
    """

    def __init__(self, policy: Union[str, Type[TD3Policy]],
                 env: Union[GymEnv, str],
                 learning_rate: Union[float, Callable] = 1e-3,
                 buffer_size: int = int(1e6),
                 learning_starts: int = 100,
                 batch_size: int = 100,
                 tau: float = 0.005,
                 gamma: float = 0.99,
                 train_freq: int = -1,
                 gradient_steps: int = -1,
                 n_episodes_rollout: int = 1,
                 action_noise: Optional[ActionNoise] = None,
                 optimize_memory_usage: bool = False,
                 policy_delay: int = 2,
                 target_policy_noise: float = 0.2,
                 target_noise_clip: float = 0.5,
                 tensorboard_log: Optional[str] = None,
                 create_eval_env: bool = False,
                 policy_kwargs: Dict[str, Any] = None,
                 verbose: int = 0,
                 seed: Optional[int] = None,
                 device: Union[th.device, str] = 'auto',
                 _init_setup_model: bool = True):

        super(TD3, self).__init__(policy, env, TD3Policy, learning_rate,
                                  buffer_size, learning_starts, batch_size,
                                  tau, gamma, train_freq, gradient_steps,
                                  n_episodes_rollout, action_noise=action_noise,
                                  policy_kwargs=policy_kwargs,
                                  tensorboard_log=tensorboard_log,
                                  verbose=verbose, device=device,
                                  create_eval_env=create_eval_env, seed=seed,
                                  sde_support=False, optimize_memory_usage=optimize_memory_usage)

        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(TD3, self)._setup_model()
        self._create_aliases()

    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:

        # Update learning rate according to lr schedule
        self._update_learning_rate([self.actor.optimizer, self.critic.optimizer])

        for gradient_step in range(gradient_steps):

            # Sample replay buffer
            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)

            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 target Q value
                target_q1, target_q2 = self.critic_target(replay_data.next_observations, next_actions)
                target_q = th.min(target_q1, target_q2)
                target_q = replay_data.rewards + (1 - replay_data.dones) * self.gamma * target_q

            # Get current Q estimates
            current_q1, current_q2 = self.critic(replay_data.observations, replay_data.actions)

            # Compute critic loss
            critic_loss = F.mse_loss(current_q1, target_q) + F.mse_loss(current_q2, target_q)

            # Optimize the critic
            self.critic.optimizer.zero_grad()
            critic_loss.backward()
            self.critic.optimizer.step()

            # Delayed policy updates
            if gradient_step % self.policy_delay == 0:
                # Compute actor loss
                actor_loss = -self.critic.q1_forward(replay_data.observations,
                                                     self.actor(replay_data.observations)).mean()

                # Optimize the actor
                self.actor.optimizer.zero_grad()
                actor_loss.backward()
                self.actor.optimizer.step()

                # Update the frozen target networks
                for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
                    target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

                for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
                    target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

        self._n_updates += gradient_steps
        logger.record("train/n_updates", self._n_updates, exclude='tensorboard')

    def learn(self,
              total_timesteps: int,
              callback: MaybeCallback = None,
              log_interval: int = 4,
              eval_env: Optional[GymEnv] = None,
              eval_freq: int = -1,
              n_eval_episodes: int = 5,
              tb_log_name: str = "TD3",
              eval_log_path: Optional[str] = None,
              reset_num_timesteps: bool = True) -> OffPolicyAlgorithm:

        return super(TD3, self).learn(total_timesteps=total_timesteps, callback=callback, log_interval=log_interval,
                                      eval_env=eval_env, eval_freq=eval_freq, n_eval_episodes=n_eval_episodes,
                                      tb_log_name=tb_log_name, eval_log_path=eval_log_path,
                                      reset_num_timesteps=reset_num_timesteps)

    def excluded_save_params(self) -> List[str]:
        """
        Returns the names of the parameters that should be excluded by default
        when saving the model.

        :return: (List[str]) List of parameters that should be excluded from save
        """
        # Exclude aliases
        return super(TD3, self).excluded_save_params() + ["actor", "critic", "actor_target", "critic_target"]

    def get_torch_variables(self) -> Tuple[List[str], List[str]]:
        """
        cf base class
        """
        state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
        return state_dicts, []