metastable-baselines2/metastable_baselines2/common/off_policy_algorithm.py

from stable_baselines3.common.off_policy_algorithm import *


class BetterOffPolicyAlgorithm(OffPolicyAlgorithm):
    """
    The base for Off-Policy algorithms (ex: SAC/TD3)

    :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
    :param env: The environment to learn from
                (if registered in Gym, can be str. Can be None for loading trained models)
    :param learning_rate: learning rate for the optimizer,
        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 policy_kwargs: Additional arguments to be passed to the policy on creation
    :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 verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
        debug messages
    :param device: Device on which the code should run.
        By default, it will try to use a Cuda compatible device and fallback to cpu
        if it is not possible.
    :param support_multi_env: Whether the algorithm supports training
        with multiple environments (as in A2C)
    :param monitor_wrapper: When creating an environment, whether to wrap it
        or not in a Monitor wrapper.
    :param seed: Seed for the pseudo random generators
    :param use_sde: Whether to use State Dependent Exploration (SDE)
        instead of action noise exploration (default: False)
    :param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
        Default: -1 (only sample at the beginning of the rollout)
    :param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
        during the warm up phase (before learning starts)
    :param sde_support: Whether the model support gSDE or not
    :param supported_action_spaces: The action spaces supported by the algorithm.
    """

    def __init__(
        self,
        policy: Union[str, Type[BasePolicy]],
        env: Union[GymEnv, str],
        learning_rate: Union[float, Schedule],
        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, "step"),
        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,
        policy_kwargs: Optional[Dict[str, Any]] = None,
        stats_window_size: int = 100,
        tensorboard_log: Optional[str] = None,
        verbose: int = 0,
        device: Union[th.device, str] = "auto",
        support_multi_env: bool = False,
        monitor_wrapper: bool = True,
        seed: Optional[int] = None,
        use_sde: bool = False,
        sde_sample_freq: int = -1,
        use_sde_at_warmup: bool = False,
        sde_support: bool = True,
        use_pca: bool = False,
        supported_action_spaces: Optional[Tuple[spaces.Space, ...]] = None,
    ):
        assert not (use_sde and use_pca)
        self.use_pca = use_pca
        policy_kwargs["use_pca"] = self.use_pca
        super().__init__(
            policy=policy,
            env=env,
            learning_rate=learning_rate,
            policy_kwargs=policy_kwargs,
            stats_window_size=stats_window_size,
            tensorboard_log=tensorboard_log,
            verbose=verbose,
            device=device,
            support_multi_env=support_multi_env,
            monitor_wrapper=monitor_wrapper,
            seed=seed,
            use_sde=use_sde,
            sde_sample_freq=sde_sample_freq,
            supported_action_spaces=supported_action_spaces,
            buffer_size=buffer_size,
            batch_size=batch_size,
            learning_starts=learning_starts,
            tau=tau,
            gamma=gamma,
            gradient_steps=gradient_steps,
            action_noise=action_noise,
            optimize_memory_usage=optimize_memory_usage,
            replay_buffer_class=replay_buffer_class,
            replay_buffer_kwargs=replay_buffer_kwargs,
            train_freq=train_freq,
            use_sde_at_warmup=use_sde_at_warmup,
            sde_support=sde_support,
        )

    def _convert_train_freq(self) -> None:
        """
        Convert `train_freq` parameter (int or tuple)
        to a TrainFreq object.
        """
        if not isinstance(self.train_freq, TrainFreq):
            train_freq = self.train_freq

            # The value of the train frequency will be checked later
            if not isinstance(train_freq, tuple):
                train_freq = (train_freq, "step")

            try:
                train_freq = (train_freq[0], TrainFrequencyUnit(train_freq[1]))
            except ValueError as e:
                raise ValueError(
                    f"The unit of the `train_freq` must be either 'step' or 'episode' not '{train_freq[1]}'!"
                ) from e

            if not isinstance(train_freq[0], int):
                raise ValueError(f"The frequency of `train_freq` must be an integer and not {train_freq[0]}")

            self.train_freq = TrainFreq(*train_freq)

    def _setup_model(self) -> None:
        self._setup_lr_schedule()
        self.set_random_seed(self.seed)

        if self.replay_buffer_class is None:
            if isinstance(self.observation_space, spaces.Dict):
                self.replay_buffer_class = DictReplayBuffer
            else:
                self.replay_buffer_class = ReplayBuffer

        if self.replay_buffer is None:
            # Make a local copy as we should not pickle
            # the environment when using HerReplayBuffer
            replay_buffer_kwargs = self.replay_buffer_kwargs.copy()
            if issubclass(self.replay_buffer_class, HerReplayBuffer):
                assert self.env is not None, "You must pass an environment when using `HerReplayBuffer`"
                replay_buffer_kwargs["env"] = self.env
            self.replay_buffer = self.replay_buffer_class(
                self.buffer_size,
                self.observation_space,
                self.action_space,
                device=self.device,
                n_envs=self.n_envs,
                optimize_memory_usage=self.optimize_memory_usage,
                **replay_buffer_kwargs,  # pytype:disable=wrong-keyword-args
            )

        self.policy = self.policy_class(  # pytype:disable=not-instantiable
            self.observation_space,
            self.action_space,
            self.lr_schedule,
            **self.policy_kwargs,  # pytype:disable=not-instantiable
        )
        self.policy = self.policy.to(self.device)

        # Convert train freq parameter to TrainFreq object
        self._convert_train_freq()

    def save_replay_buffer(self, path: Union[str, pathlib.Path, io.BufferedIOBase]) -> None:
        """
        Save the replay buffer as a pickle file.

        :param path: Path to the file where the replay buffer should be saved.
            if path is a str or pathlib.Path, the path is automatically created if necessary.
        """
        assert self.replay_buffer is not None, "The replay buffer is not defined"
        save_to_pkl(path, self.replay_buffer, self.verbose)

    def load_replay_buffer(
        self,
        path: Union[str, pathlib.Path, io.BufferedIOBase],
        truncate_last_traj: bool = True,
    ) -> None:
        """
        Load a replay buffer from a pickle file.

        :param path: Path to the pickled replay buffer.
        :param truncate_last_traj: When using ``HerReplayBuffer`` with online sampling:
            If set to ``True``, we assume that the last trajectory in the replay buffer was finished
            (and truncate it).
            If set to ``False``, we assume that we continue the same trajectory (same episode).
        """
        self.replay_buffer = load_from_pkl(path, self.verbose)
        assert isinstance(self.replay_buffer, ReplayBuffer), "The replay buffer must inherit from ReplayBuffer class"

        # Backward compatibility with SB3 < 2.1.0 replay buffer
        # Keep old behavior: do not handle timeout termination separately
        if not hasattr(self.replay_buffer, "handle_timeout_termination"):  # pragma: no cover
            self.replay_buffer.handle_timeout_termination = False
            self.replay_buffer.timeouts = np.zeros_like(self.replay_buffer.dones)

        if isinstance(self.replay_buffer, HerReplayBuffer):
            assert self.env is not None, "You must pass an environment at load time when using `HerReplayBuffer`"
            self.replay_buffer.set_env(self.get_env())
            if truncate_last_traj:
                self.replay_buffer.truncate_last_trajectory()

    def _setup_learn(
        self,
        total_timesteps: int,
        callback: MaybeCallback = None,
        reset_num_timesteps: bool = True,
        tb_log_name: str = "run",
        progress_bar: bool = False,
    ) -> Tuple[int, BaseCallback]:
        """
        cf `BaseAlgorithm`.
        """
        # Prevent continuity issue by truncating trajectory
        # when using memory efficient replay buffer
        # see https://github.com/DLR-RM/stable-baselines3/issues/46

        replay_buffer = self.replay_buffer

        truncate_last_traj = (
            self.optimize_memory_usage
            and reset_num_timesteps
            and replay_buffer is not None
            and (replay_buffer.full or replay_buffer.pos > 0)
        )

        if truncate_last_traj:
            warnings.warn(
                "The last trajectory in the replay buffer will be truncated, "
                "see https://github.com/DLR-RM/stable-baselines3/issues/46."
                "You should use `reset_num_timesteps=False` or `optimize_memory_usage=False`"
                "to avoid that issue."
            )
            # Go to the previous index
            pos = (replay_buffer.pos - 1) % replay_buffer.buffer_size
            replay_buffer.dones[pos] = True

        return super()._setup_learn(
            total_timesteps,
            callback,
            reset_num_timesteps,
            tb_log_name,
            progress_bar,
        )

    def learn(
        self: SelfOffPolicyAlgorithm,
        total_timesteps: int,
        callback: MaybeCallback = None,
        log_interval: int = 4,
        tb_log_name: str = "run",
        reset_num_timesteps: bool = True,
        progress_bar: bool = False,
    ) -> SelfOffPolicyAlgorithm:
        total_timesteps, callback = self._setup_learn(
            total_timesteps,
            callback,
            reset_num_timesteps,
            tb_log_name,
            progress_bar,
        )

        callback.on_training_start(locals(), globals())

        while self.num_timesteps < total_timesteps:
            rollout = self.collect_rollouts(
                self.env,
                train_freq=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

            if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts:
                # If no `gradient_steps` is specified,
                # do as many gradients steps as steps performed during the rollout
                gradient_steps = self.gradient_steps if self.gradient_steps >= 0 else rollout.episode_timesteps
                # Special case when the user passes `gradient_steps=0`
                if gradient_steps > 0:
                    self.train(batch_size=self.batch_size, gradient_steps=gradient_steps)

        callback.on_training_end()

        return self

    def train(self, gradient_steps: int, batch_size: int) -> None:
        """
        Sample the replay buffer and do the updates
        (gradient descent and update target networks)
        """
        raise NotImplementedError()

    def _sample_action(
        self,
        learning_starts: int,
        action_noise: Optional[ActionNoise] = None,
        n_envs: int = 1,
    ) -> Tuple[np.ndarray, np.ndarray]:
        """
        Sample an action according to the exploration policy.
        This is either done by sampling the probability distribution of the policy,
        or sampling a random action (from a uniform distribution over the action space)
        or by adding noise to the deterministic output.

        :param action_noise: Action noise that will be used for exploration
            Required for deterministic policy (e.g. TD3). This can also be used
            in addition to the stochastic policy for SAC.
        :param learning_starts: Number of steps before learning for the warm-up phase.
        :param n_envs:
        :return: action to take in the environment
            and scaled action that will be stored in the replay buffer.
            The two differs when the action space is not normalized (bounds are not [-1, 1]).
        """
        # Select action randomly or according to policy
        if self.num_timesteps < learning_starts and not (self.use_sde and self.use_sde_at_warmup):
            # Warmup phase
            unscaled_action = np.array([self.action_space.sample() for _ in range(n_envs)])
        else:
            # Note: when using continuous actions,
            # we assume that the policy uses tanh to scale the action
            # We use non-deterministic action in the case of SAC, for TD3, it does not matter
            unscaled_action, _ = self.predict(self._last_obs, deterministic=False)

        # Rescale the action from [low, high] to [-1, 1]
        if isinstance(self.action_space, spaces.Box):
            scaled_action = self.policy.scale_action(unscaled_action)

            # Add noise to the action (improve exploration)
            if action_noise is not None:
                scaled_action = np.clip(scaled_action + action_noise(), -1, 1)

            # We store the scaled action in the buffer
            buffer_action = scaled_action
            action = self.policy.unscale_action(scaled_action)
        else:
            # Discrete case, no need to normalize or clip
            buffer_action = unscaled_action
            action = buffer_action
        return action, buffer_action

    def _dump_logs(self) -> None:
        """
        Write log.
        """
        time_elapsed = max((time.time_ns() - self.start_time) / 1e9, sys.float_info.epsilon)
        fps = int((self.num_timesteps - self._num_timesteps_at_start) / time_elapsed)
        self.logger.record("time/episodes", self._episode_num, exclude="tensorboard")
        if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
            self.logger.record("rollout/ep_rew_mean", safe_mean([ep_info["r"] for ep_info in self.ep_info_buffer]))
            self.logger.record("rollout/ep_len_mean", safe_mean([ep_info["l"] for ep_info in self.ep_info_buffer]))
        self.logger.record("time/fps", fps)
        self.logger.record("time/time_elapsed", int(time_elapsed), exclude="tensorboard")
        self.logger.record("time/total_timesteps", self.num_timesteps, exclude="tensorboard")
        if self.use_sde or self.use_pca:
            self.logger.record("train/std", (self.actor.get_std()).mean().item())

        if len(self.ep_success_buffer) > 0:
            self.logger.record("rollout/success_rate", safe_mean(self.ep_success_buffer))
        # Pass the number of timesteps for tensorboard
        self.logger.dump(step=self.num_timesteps)

    def _on_step(self) -> None:
        """
        Method called after each step in the environment.
        It is meant to trigger DQN target network update
        but can be used for other purposes
        """
        pass

    def _store_transition(
        self,
        replay_buffer: ReplayBuffer,
        buffer_action: np.ndarray,
        new_obs: Union[np.ndarray, Dict[str, np.ndarray]],
        reward: np.ndarray,
        dones: np.ndarray,
        infos: List[Dict[str, Any]],
    ) -> None:
        """
        Store transition in the replay buffer.
        We store the normalized action and the unnormalized observation.
        It also handles terminal observations (because VecEnv resets automatically).

        :param replay_buffer: Replay buffer object where to store the transition.
        :param buffer_action: normalized action
        :param new_obs: next observation in the current episode
            or first observation of the episode (when dones is True)
        :param reward: reward for the current transition
        :param dones: Termination signal
        :param infos: List of additional information about the transition.
            It may contain the terminal observations and information about timeout.
        """
        # Store only the unnormalized version
        if self._vec_normalize_env is not None:
            new_obs_ = self._vec_normalize_env.get_original_obs()
            reward_ = self._vec_normalize_env.get_original_reward()
        else:
            # Avoid changing the original ones
            self._last_original_obs, new_obs_, reward_ = self._last_obs, new_obs, reward

        # Avoid modification by reference
        next_obs = deepcopy(new_obs_)
        # As the VecEnv resets automatically, new_obs is already the
        # first observation of the next episode
        for i, done in enumerate(dones):
            if done and infos[i].get("terminal_observation") is not None:
                if isinstance(next_obs, dict):
                    next_obs_ = infos[i]["terminal_observation"]
                    # VecNormalize normalizes the terminal observation
                    if self._vec_normalize_env is not None:
                        next_obs_ = self._vec_normalize_env.unnormalize_obs(next_obs_)
                    # Replace next obs for the correct envs
                    for key in next_obs.keys():
                        next_obs[key][i] = next_obs_[key]
                else:
                    next_obs[i] = infos[i]["terminal_observation"]
                    # VecNormalize normalizes the terminal observation
                    if self._vec_normalize_env is not None:
                        next_obs[i] = self._vec_normalize_env.unnormalize_obs(next_obs[i, :])

        replay_buffer.add(
            self._last_original_obs,
            next_obs,
            buffer_action,
            reward_,
            dones,
            infos,
        )

        self._last_obs = new_obs
        # Save the unnormalized observation
        if self._vec_normalize_env is not None:
            self._last_original_obs = new_obs_

    def collect_rollouts(
        self,
        env: VecEnv,
        callback: BaseCallback,
        train_freq: TrainFreq,
        replay_buffer: ReplayBuffer,
        action_noise: Optional[ActionNoise] = None,
        learning_starts: int = 0,
        log_interval: Optional[int] = None,
    ) -> RolloutReturn:
        """
        Collect experiences and store them into a ``ReplayBuffer``.

        :param env: The training environment
        :param callback: Callback that will be called at each step
            (and at the beginning and end of the rollout)
        :param train_freq: How much experience to collect
            by doing rollouts of current policy.
            Either ``TrainFreq(<n>, TrainFrequencyUnit.STEP)``
            or ``TrainFreq(<n>, TrainFrequencyUnit.EPISODE)``
            with ``<n>`` being an integer greater than 0.
        :param action_noise: Action noise that will be used for exploration
            Required for deterministic policy (e.g. TD3). This can also be used
            in addition to the stochastic policy for SAC.
        :param learning_starts: Number of steps before learning for the warm-up phase.
        :param replay_buffer:
        :param log_interval: Log data every ``log_interval`` episodes
        :return:
        """
        # Switch to eval mode (this affects batch norm / dropout)
        self.policy.set_training_mode(False)

        num_collected_steps, num_collected_episodes = 0, 0

        assert isinstance(env, VecEnv), "You must pass a VecEnv"
        assert train_freq.frequency > 0, "Should at least collect one step or episode."

        if env.num_envs > 1:
            assert train_freq.unit == TrainFrequencyUnit.STEP, "You must use only one env when doing episodic training."

        # Vectorize action noise if needed
        if action_noise is not None and env.num_envs > 1 and not isinstance(action_noise, VectorizedActionNoise):
            action_noise = VectorizedActionNoise(action_noise, env.num_envs)

        if self.use_sde or self.use_pca:
            self.actor.reset_noise(env.num_envs)

        callback.on_rollout_start()
        continue_training = True
        while should_collect_more_steps(train_freq, num_collected_steps, num_collected_episodes):
            if (self.use_sde or self.use_pca) and self.sde_sample_freq > 0 and num_collected_steps % self.sde_sample_freq == 0:
                # Sample a new noise matrix
                self.actor.reset_noise(env.num_envs)

            # Select action randomly or according to policy
            actions, buffer_actions = self._sample_action(learning_starts, action_noise, env.num_envs)

            # Rescale and perform action
            new_obs, rewards, dones, infos = env.step(actions)

            self.num_timesteps += env.num_envs
            num_collected_steps += 1

            # Give access to local variables
            callback.update_locals(locals())
            # Only stop training if return value is False, not when it is None.
            if callback.on_step() is False:
                return RolloutReturn(num_collected_steps * env.num_envs, num_collected_episodes, continue_training=False)

            # Retrieve reward and episode length if using Monitor wrapper
            self._update_info_buffer(infos, dones)

            # Store data in replay buffer (normalized action and unnormalized observation)
            self._store_transition(replay_buffer, buffer_actions, new_obs, rewards, dones, infos)

            self._update_current_progress_remaining(self.num_timesteps, self._total_timesteps)

            # For DQN, check if the target network should be updated
            # and update the exploration schedule
            # For SAC/TD3, the update is dones as the same time as the gradient update
            # see https://github.com/hill-a/stable-baselines/issues/900
            self._on_step()

            for idx, done in enumerate(dones):
                if done:
                    # Update stats
                    num_collected_episodes += 1
                    self._episode_num += 1

                    if action_noise is not None:
                        kwargs = dict(indices=[idx]) if env.num_envs > 1 else {}
                        action_noise.reset(**kwargs)

                    # Log training infos
                    if log_interval is not None and self._episode_num % log_interval == 0:
                        self._dump_logs()
        callback.on_rollout_end()

        return RolloutReturn(num_collected_steps * env.num_envs, num_collected_episodes, continue_training)