stable-baselines3/stable_baselines3/sac/sac.py

from typing import List, Tuple, Type, Union, Callable, Optional, Dict, Any

import torch as th
import torch.nn.functional as F
import numpy as np

from stable_baselines3.common import logger
from stable_baselines3.common.base_class import OffPolicyRLModel
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
from stable_baselines3.common.noise import ActionNoise
from stable_baselines3.sac.policies import SACPolicy


class SAC(OffPolicyRLModel):
    """
    Soft Actor-Critic (SAC)
    Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
    This implementation borrows code from original implementation (https://github.com/haarnoja/sac)
    from OpenAI Spinning Up (https://github.com/openai/spinningup), from the softlearning repo
    (https://github.com/rail-berkeley/softlearning/)
    and from Stable Baselines (https://github.com/hill-a/stable-baselines)
    Paper: https://arxiv.org/abs/1801.01290
    Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html

    Note: we use double q target and not value target as discussed
    in https://github.com/hill-a/stable-baselines/issues/270

    :param policy: (SACPolicy 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 (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 ent_coef: (str or float) Entropy regularization coefficient. (Equivalent to
        inverse of reward scale in the original SAC paper.)  Controlling exploration/exploitation trade-off.
        Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
    :param target_update_interval: (int) update the target network every ``target_network_update_freq`` steps.
    :param target_entropy: (str or float) target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
    :param use_sde: (bool) Whether to use State Dependent Exploration (SDE)
        instead of action noise exploration (default: False)
    :param sde_sample_freq: (int) Sample a new noise matrix every n steps when using SDE
        Default: -1 (only sample at the beginning of the rollout)
    :param use_sde_at_warmup: (bool) Whether to use SDE instead of uniform sampling
        during the warm up phase (before learning starts)
    :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[SACPolicy]],
                 env: Union[GymEnv, str],
                 learning_rate: Union[float, Callable] = 3e-4,
                 buffer_size: int = int(1e6),
                 learning_starts: int = 100,
                 batch_size: int = 256,
                 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,
                 ent_coef: Union[str, float] = 'auto',
                 target_update_interval: int = 1,
                 target_entropy: Union[str, float] = 'auto',
                 use_sde: bool = False,
                 sde_sample_freq: int = -1,
                 use_sde_at_warmup: bool = False,
                 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(SAC, self).__init__(policy, env, SACPolicy, learning_rate,
                                  buffer_size, learning_starts, batch_size,
                                  policy_kwargs, verbose, device,
                                  create_eval_env=create_eval_env, seed=seed,
                                  use_sde=use_sde, sde_sample_freq=sde_sample_freq,
                                  use_sde_at_warmup=use_sde_at_warmup)

        self.target_entropy = target_entropy
        self.log_ent_coef = None  # type: Optional[th.Tensor]
        self.target_update_interval = target_update_interval
        self.tau = tau
        # Entropy coefficient / Entropy temperature
        # Inverse of the reward scale
        self.ent_coef = ent_coef
        self.target_update_interval = target_update_interval
        self.train_freq = train_freq
        self.gradient_steps = gradient_steps
        self.n_episodes_rollout = n_episodes_rollout
        self.action_noise = action_noise
        self.gamma = gamma
        self.ent_coef_optimizer = None

        if _init_setup_model:
            self._setup_model()

    def _setup_model(self) -> None:
        super(SAC, self)._setup_model()
        self._create_aliases()

        # Target entropy is used when learning the entropy coefficient
        if self.target_entropy == 'auto':
            # automatically set target entropy if needed
            self.target_entropy = -np.prod(self.env.action_space.shape).astype(np.float32)
        else:
            # Force conversion
            # this will also throw an error for unexpected string
            self.target_entropy = float(self.target_entropy)

        # The entropy coefficient or entropy can be learned automatically
        # see Automating Entropy Adjustment for Maximum Entropy RL section
        # of https://arxiv.org/abs/1812.05905
        if isinstance(self.ent_coef, str) and self.ent_coef.startswith('auto'):
            # Default initial value of ent_coef when learned
            init_value = 1.0
            if '_' in self.ent_coef:
                init_value = float(self.ent_coef.split('_')[1])
                assert init_value > 0., "The initial value of ent_coef must be greater than 0"

            # Note: we optimize the log of the entropy coeff which is slightly different from the paper
            # as discussed in https://github.com/rail-berkeley/softlearning/issues/37
            self.log_ent_coef = th.log(th.ones(1, device=self.device) * init_value).requires_grad_(True)
            self.ent_coef_optimizer = th.optim.Adam([self.log_ent_coef], lr=self.lr_schedule(1))
        else:
            # Force conversion to float
            # this will throw an error if a malformed string (different from 'auto')
            # is passed
            self.ent_coef_tensor = th.tensor(float(self.ent_coef)).to(self.device)

    def _create_aliases(self) -> None:
        self.actor = self.policy.actor
        self.critic = self.policy.critic
        self.critic_target = self.policy.critic_target

    def train(self, gradient_steps: int, batch_size: int = 64) -> None:
        # Update optimizers learning rate
        optimizers = [self.actor.optimizer, self.critic.optimizer]
        if self.ent_coef_optimizer is not None:
            optimizers += [self.ent_coef_optimizer]

        # Update learning rate according to lr schedule
        self._update_learning_rate(optimizers)

        ent_coef_losses, ent_coefs = [], []
        actor_losses, critic_losses = [], []

        for gradient_step in range(gradient_steps):
            # Sample replay buffer
            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)

            # We need to sample because `log_std` may have changed between two gradient steps
            if self.use_sde:
                self.actor.reset_noise()

            # Action by the current actor for the sampled state
            actions_pi, log_prob = self.actor.action_log_prob(replay_data.observations)
            log_prob = log_prob.reshape(-1, 1)

            ent_coef_loss = None
            if self.ent_coef_optimizer is not None:
                # Important: detach the variable from the graph
                # so we don't change it with other losses
                # see https://github.com/rail-berkeley/softlearning/issues/60
                ent_coef = th.exp(self.log_ent_coef.detach())
                ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean()
                ent_coef_losses.append(ent_coef_loss.item())
            else:
                ent_coef = self.ent_coef_tensor

            ent_coefs.append(ent_coef.item())

            # Optimize entropy coefficient, also called
            # entropy temperature or alpha in the paper
            if ent_coef_loss is not None:
                self.ent_coef_optimizer.zero_grad()
                ent_coef_loss.backward()
                self.ent_coef_optimizer.step()

            with th.no_grad():
                # Select action according to policy
                next_actions, next_log_prob = self.actor.action_log_prob(replay_data.next_observations)
                # 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
                # td error + entropy term
                q_backup = target_q - ent_coef * next_log_prob.reshape(-1, 1)

            # Get current Q estimates
            # using action from the replay buffer
            current_q1, current_q2 = self.critic(replay_data.observations, replay_data.actions)

            # Compute critic loss
            critic_loss = 0.5 * (F.mse_loss(current_q1, q_backup) + F.mse_loss(current_q2, q_backup))
            critic_losses.append(critic_loss.item())

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

            # Compute actor loss
            # Alternative: actor_loss = th.mean(log_prob - qf1_pi)
            qf1_pi, qf2_pi = self.critic.forward(replay_data.observations, actions_pi)
            min_qf_pi = th.min(qf1_pi, qf2_pi)
            actor_loss = (ent_coef * log_prob - min_qf_pi).mean()
            actor_losses.append(actor_loss.item())

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

            # Update target networks
            if gradient_step % self.target_update_interval == 0:
                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)

        self._n_updates += gradient_steps

        logger.logkv("n_updates", self._n_updates)
        logger.logkv("ent_coef", np.mean(ent_coefs))
        logger.logkv("actor_loss", np.mean(actor_losses))
        logger.logkv("critic_loss", np.mean(critic_losses))
        if len(ent_coef_losses) > 0:
            logger.logkv("ent_coef_loss", np.mean(ent_coef_losses))

    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 = "SAC",
              eval_log_path: Optional[str] = None,
              reset_num_timesteps: bool = True) -> OffPolicyRLModel:

        callback = self._setup_learn(eval_env, callback, eval_freq,
                                     n_eval_episodes, eval_log_path, reset_num_timesteps)
        callback.on_training_start(locals(), globals())

        while self.num_timesteps < total_timesteps:
            rollout = self.collect_rollouts(self.env, n_episodes=self.n_episodes_rollout,
                                            n_steps=self.train_freq, action_noise=self.action_noise,
                                            callback=callback,
                                            learning_starts=self.learning_starts,
                                            replay_buffer=self.replay_buffer,
                                            log_interval=log_interval)

            if rollout.continue_training is False:
                break

            self._update_current_progress(self.num_timesteps, total_timesteps)

            if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts:
                gradient_steps = self.gradient_steps if self.gradient_steps > 0 else rollout.episode_timesteps
                self.train(gradient_steps, batch_size=self.batch_size)

        callback.on_training_end()
        return self

    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(SAC, self).excluded_save_params() + ["actor", "critic", "critic_target"]

    def get_torch_variables(self) -> Tuple[List[str], List[str]]:
        """
        cf base class
        """
        state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
        saved_tensors = ['log_ent_coef']
        if self.ent_coef_optimizer is not None:
            state_dicts.append('ent_coef_optimizer')
        else:
            saved_tensors.append('ent_coef_tensor')
        return state_dicts, saved_tensors