base_class.py

"""Abstract base classes for RL algorithms."""

import io
import pathlib
import time
from abc import ABC, abstractmethod
from collections import deque
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union

import gym
import numpy as np
import torch as th

from stable_baselines3.common import utils
from stable_baselines3.common.callbacks import BaseCallback, CallbackList, ConvertCallback, EvalCallback
from stable_baselines3.common.env_util import is_wrapped
from stable_baselines3.common.logger import Logger
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.noise import ActionNoise
from stable_baselines3.common.policies import BasePolicy, get_policy_from_name
from stable_baselines3.common.preprocessing import check_for_nested_spaces, is_image_space, is_image_space_channels_first
from stable_baselines3.common.save_util import load_from_zip_file, recursive_getattr, recursive_setattr, save_to_zip_file
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import (
    check_for_correct_spaces,
    get_device,
    get_schedule_fn,
    get_system_info,
    set_random_seed,
    update_learning_rate,
)
from stable_baselines3.common.vec_env import (
    DummyVecEnv,
    VecEnv,
    VecNormalize,
    VecTransposeImage,
    is_vecenv_wrapped,
    unwrap_vec_normalize,
)


def maybe_make_env(env: Union[GymEnv, str, None], verbose: int) -> Optional[GymEnv]:
    """If env is a string, make the environment; otherwise, return env.

    :param env: The environment to learn from.
    :param verbose: logging verbosity
    :return A Gym (vector) environment.
    """
    if isinstance(env, str):
        if verbose >= 1:
            print(f"Creating environment from the given name '{env}'")
        env = gym.make(env)
    return env


class BaseAlgorithm(ABC):
    """
    The base of RL algorithms

    :param policy: Policy object
    :param env: The environment to learn from
                (if registered in Gym, can be str. Can be None for loading trained models)
    :param policy_base: The base policy used by this method
    :param learning_rate: learning rate for the optimizer,
        it can be a function of the current progress remaining (from 1 to 0)
    :param policy_kwargs: Additional arguments to be passed to the policy on creation
    :param tensorboard_log: the log location for tensorboard (if None, no logging)
    :param verbose: The verbosity level: 0 none, 1 training information, 2 debug
    :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 create_eval_env: Whether to create a second environment that will be
        used for evaluating the agent periodically. (Only available when passing string for the environment)
    :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 generalized State Dependent Exploration (gSDE)
        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 supported_action_spaces: The action spaces supported by the algorithm.
    """

    def __init__(
        self,
        policy: Type[BasePolicy],
        env: Union[GymEnv, str, None],
        policy_base: Type[BasePolicy],
        learning_rate: Union[float, Schedule],
        policy_kwargs: Optional[Dict[str, Any]] = None,
        tensorboard_log: Optional[str] = None,
        verbose: int = 0,
        device: Union[th.device, str] = "auto",
        support_multi_env: bool = False,
        create_eval_env: bool = False,
        monitor_wrapper: bool = True,
        seed: Optional[int] = None,
        use_sde: bool = False,
        sde_sample_freq: int = -1,
        supported_action_spaces: Optional[Tuple[gym.spaces.Space, ...]] = None,
    ):

        if isinstance(policy, str) and policy_base is not None:
            self.policy_class = get_policy_from_name(policy_base, policy)
        else:
            self.policy_class = policy

        self.device = get_device(device)
        if verbose > 0:
            print(f"Using {self.device} device")

        self.env = None  # type: Optional[GymEnv]
        # get VecNormalize object if needed
        self._vec_normalize_env = unwrap_vec_normalize(env)
        self.verbose = verbose
        self.policy_kwargs = {} if policy_kwargs is None else policy_kwargs
        self.observation_space = None  # type: Optional[gym.spaces.Space]
        self.action_space = None  # type: Optional[gym.spaces.Space]
        self.n_envs = None
        self.num_timesteps = 0
        # Used for updating schedules
        self._total_timesteps = 0
        # Used for computing fps, it is updated at each call of learn()
        self._num_timesteps_at_start = 0
        self.eval_env = None
        self.seed = seed
        self.action_noise = None  # type: Optional[ActionNoise]
        self.start_time = None
        self.policy = None
        self.learning_rate = learning_rate
        self.tensorboard_log = tensorboard_log
        self.lr_schedule = None  # type: Optional[Schedule]
        self._last_obs = None  # type: Optional[Union[np.ndarray, Dict[str, np.ndarray]]]
        self._last_episode_starts = None  # type: Optional[np.ndarray]
        # When using VecNormalize:
        self._last_original_obs = None  # type: Optional[Union[np.ndarray, Dict[str, np.ndarray]]]
        self._episode_num = 0
        # Used for gSDE only
        self.use_sde = use_sde
        self.sde_sample_freq = sde_sample_freq
        # Track the training progress remaining (from 1 to 0)
        # this is used to update the learning rate
        self._current_progress_remaining = 1
        # Buffers for logging
        self.ep_info_buffer = None  # type: Optional[deque]
        self.ep_success_buffer = None  # type: Optional[deque]
        # For logging (and TD3 delayed updates)
        self._n_updates = 0  # type: int
        # The logger object
        self._logger = None  # type: Logger
        # Whether the user passed a custom logger or not
        self._custom_logger = False

        # Create and wrap the env if needed
        if env is not None:
            if isinstance(env, str):
                if create_eval_env:
                    self.eval_env = maybe_make_env(env, self.verbose)

            env = maybe_make_env(env, self.verbose)
            env = self._wrap_env(env, self.verbose, monitor_wrapper)

            self.observation_space = env.observation_space
            self.action_space = env.action_space
            self.n_envs = env.num_envs
            self.env = env

            if supported_action_spaces is not None:
                assert isinstance(self.action_space, supported_action_spaces), (
                    f"The algorithm only supports {supported_action_spaces} as action spaces "
                    f"but {self.action_space} was provided"
                )

            if not support_multi_env and self.n_envs > 1:
                raise ValueError(
                    "Error: the model does not support multiple envs; it requires " "a single vectorized environment."
                )

            # Catch common mistake: using MlpPolicy/CnnPolicy instead of MultiInputPolicy
            if policy in ["MlpPolicy", "CnnPolicy"] and isinstance(self.observation_space, gym.spaces.Dict):
                raise ValueError(f"You must use `MultiInputPolicy` when working with dict observation space, not {policy}")

            if self.use_sde and not isinstance(self.action_space, gym.spaces.Box):
                raise ValueError("generalized State-Dependent Exploration (gSDE) can only be used with continuous actions.")

    @staticmethod
    def _wrap_env(env: GymEnv, verbose: int = 0, monitor_wrapper: bool = True) -> VecEnv:
        """ "
        Wrap environment with the appropriate wrappers if needed.
        For instance, to have a vectorized environment
        or to re-order the image channels.

        :param env:
        :param verbose:
        :param monitor_wrapper: Whether to wrap the env in a ``Monitor`` when possible.
        :return: The wrapped environment.
        """
        if not isinstance(env, VecEnv):
            if not is_wrapped(env, Monitor) and monitor_wrapper:
                if verbose >= 1:
                    print("Wrapping the env with a `Monitor` wrapper")
                env = Monitor(env)
            if verbose >= 1:
                print("Wrapping the env in a DummyVecEnv.")
            env = DummyVecEnv([lambda: env])

        # Make sure that dict-spaces are not nested (not supported)
        check_for_nested_spaces(env.observation_space)

        if isinstance(env.observation_space, gym.spaces.Dict):
            for space in env.observation_space.spaces.values():
                if isinstance(space, gym.spaces.Dict):
                    raise ValueError("Nested observation spaces are not supported (Dict spaces inside Dict space).")

        if not is_vecenv_wrapped(env, VecTransposeImage):
            wrap_with_vectranspose = False
            if isinstance(env.observation_space, gym.spaces.Dict):
                # If even one of the keys is a image-space in need of transpose, apply transpose
                # If the image spaces are not consistent (for instance one is channel first,
                # the other channel last), VecTransposeImage will throw an error
                for space in env.observation_space.spaces.values():
                    wrap_with_vectranspose = wrap_with_vectranspose or (
                        is_image_space(space) and not is_image_space_channels_first(space)
                    )
            else:
                wrap_with_vectranspose = is_image_space(env.observation_space) and not is_image_space_channels_first(
                    env.observation_space
                )

            if wrap_with_vectranspose:
                if verbose >= 1:
                    print("Wrapping the env in a VecTransposeImage.")
                env = VecTransposeImage(env)

        return env

    @abstractmethod
    def _setup_model(self) -> None:
        """Create networks, buffer and optimizers."""

    def set_logger(self, logger: Logger) -> None:
        """
        Setter for for logger object.

        .. warning::

          When passing a custom logger object,
          this will overwrite ``tensorboard_log`` and ``verbose`` settings
          passed to the constructor.
        """
        self._logger = logger
        # User defined logger
        self._custom_logger = True

    @property
    def logger(self) -> Logger:
        """Getter for the logger object."""
        return self._logger

    def _get_eval_env(self, eval_env: Optional[GymEnv]) -> Optional[GymEnv]:
        """
        Return the environment that will be used for evaluation.

        :param eval_env:)
        :return:
        """
        if eval_env is None:
            eval_env = self.eval_env

        if eval_env is not None:
            eval_env = self._wrap_env(eval_env, self.verbose)
            assert eval_env.num_envs == 1
        return eval_env

    def _setup_lr_schedule(self) -> None:
        """Transform to callable if needed."""
        self.lr_schedule = get_schedule_fn(self.learning_rate)

    def _update_current_progress_remaining(self, num_timesteps: int, total_timesteps: int) -> None:
        """
        Compute current progress remaining (starts from 1 and ends to 0)

        :param num_timesteps: current number of timesteps
        :param total_timesteps:
        """
        self._current_progress_remaining = 1.0 - float(num_timesteps) / float(total_timesteps)

    def _update_learning_rate(self, optimizers: Union[List[th.optim.Optimizer], th.optim.Optimizer]) -> None:
        """
        Update the optimizers learning rate using the current learning rate schedule
        and the current progress remaining (from 1 to 0).

        :param optimizers:
            An optimizer or a list of optimizers.
        """
        # Log the current learning rate
        self.logger.record("train/learning_rate", self.lr_schedule(self._current_progress_remaining))

        if not isinstance(optimizers, list):
            optimizers = [optimizers]
        for optimizer in optimizers:
            update_learning_rate(optimizer, self.lr_schedule(self._current_progress_remaining))

    def _excluded_save_params(self) -> List[str]:
        """
        Returns the names of the parameters that should be excluded from being
        saved by pickling. E.g. replay buffers are skipped by default
        as they take up a lot of space. PyTorch variables should be excluded
        with this so they can be stored with ``th.save``.

        :return: List of parameters that should be excluded from being saved with pickle.
        """
        return [
            "policy",
            "device",
            "env",
            "eval_env",
            "replay_buffer",
            "rollout_buffer",
            "_vec_normalize_env",
            "_episode_storage",
            "_logger",
            "_custom_logger",
        ]

    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
        """
        Get the name of the torch variables that will be saved with
        PyTorch ``th.save``, ``th.load`` and ``state_dicts`` instead of the default
        pickling strategy. This is to handle device placement correctly.

        Names can point to specific variables under classes, e.g.
        "policy.optimizer" would point to ``optimizer`` object of ``self.policy``
        if this object.

        :return:
            List of Torch variables whose state dicts to save (e.g. th.nn.Modules),
            and list of other Torch variables to store with ``th.save``.
        """
        state_dicts = ["policy"]

        return state_dicts, []

    def _init_callback(
        self,
        callback: MaybeCallback,
        eval_env: Optional[VecEnv] = None,
        eval_freq: int = 10000,
        n_eval_episodes: int = 5,
        log_path: Optional[str] = None,
    ) -> BaseCallback:
        """
        :param callback: Callback(s) called at every step with state of the algorithm.
        :param eval_freq: How many steps between evaluations; if None, do not evaluate.
        :param n_eval_episodes: How many episodes to play per evaluation
        :param n_eval_episodes: Number of episodes to rollout during evaluation.
        :param log_path: Path to a folder where the evaluations will be saved
        :return: A hybrid callback calling `callback` and performing evaluation.
        """
        # Convert a list of callbacks into a callback
        if isinstance(callback, list):
            callback = CallbackList(callback)

        # Convert functional callback to object
        if not isinstance(callback, BaseCallback):
            callback = ConvertCallback(callback)

        # Create eval callback in charge of the evaluation
        if eval_env is not None:
            eval_callback = EvalCallback(
                eval_env,
                best_model_save_path=log_path,
                log_path=log_path,
                eval_freq=eval_freq,
                n_eval_episodes=n_eval_episodes,
            )
            callback = CallbackList([callback, eval_callback])

        callback.init_callback(self)
        return callback

    def _setup_learn(
        self,
        total_timesteps: int,
        eval_env: Optional[GymEnv],
        callback: MaybeCallback = None,
        eval_freq: int = 10000,
        n_eval_episodes: int = 5,
        log_path: Optional[str] = None,
        reset_num_timesteps: bool = True,
        tb_log_name: str = "run",
    ) -> Tuple[int, BaseCallback]:
        """
        Initialize different variables needed for training.

        :param total_timesteps: The total number of samples (env steps) to train on
        :param eval_env: Environment to use for evaluation.
        :param callback: Callback(s) called at every step with state of the algorithm.
        :param eval_freq: How many steps between evaluations
        :param n_eval_episodes: How many episodes to play per evaluation
        :param log_path: Path to a folder where the evaluations will be saved
        :param reset_num_timesteps: Whether to reset or not the ``num_timesteps`` attribute
        :param tb_log_name: the name of the run for tensorboard log
        :return:
        """
        self.start_time = time.time()

        if self.ep_info_buffer is None or reset_num_timesteps:
            # Initialize buffers if they don't exist, or reinitialize if resetting counters
            self.ep_info_buffer = deque(maxlen=100)
            self.ep_success_buffer = deque(maxlen=100)

        if self.action_noise is not None:
            self.action_noise.reset()

        if reset_num_timesteps:
            self.num_timesteps = 0
            self._episode_num = 0
        else:
            # Make sure training timesteps are ahead of the internal counter
            total_timesteps += self.num_timesteps
        self._total_timesteps = total_timesteps
        self._num_timesteps_at_start = self.num_timesteps

        # Avoid resetting the environment when calling ``.learn()`` consecutive times
        if reset_num_timesteps or self._last_obs is None:
            self._last_obs = self.env.reset()  # pytype: disable=annotation-type-mismatch
            self._last_episode_starts = np.ones((self.env.num_envs,), dtype=bool)
            # Retrieve unnormalized observation for saving into the buffer
            if self._vec_normalize_env is not None:
                self._last_original_obs = self._vec_normalize_env.get_original_obs()

        if eval_env is not None and self.seed is not None:
            eval_env.seed(self.seed)

        eval_env = self._get_eval_env(eval_env)

        # Configure logger's outputs if no logger was passed
        if not self._custom_logger:
            self._logger = utils.configure_logger(self.verbose, self.tensorboard_log, tb_log_name, reset_num_timesteps)

        # Create eval callback if needed
        callback = self._init_callback(callback, eval_env, eval_freq, n_eval_episodes, log_path)

        return total_timesteps, callback

    def _update_info_buffer(self, infos: List[Dict[str, Any]], dones: Optional[np.ndarray] = None) -> None:
        """
        Retrieve reward, episode length, episode success and update the buffer
        if using Monitor wrapper or a GoalEnv.

        :param infos: List of additional information about the transition.
        :param dones: Termination signals
        """
        if dones is None:
            dones = np.array([False] * len(infos))
        for idx, info in enumerate(infos):
            maybe_ep_info = info.get("episode")
            maybe_is_success = info.get("is_success")
            if maybe_ep_info is not None:
                self.ep_info_buffer.extend([maybe_ep_info])
            if maybe_is_success is not None and dones[idx]:
                self.ep_success_buffer.append(maybe_is_success)

    def get_env(self) -> Optional[VecEnv]:
        """
        Returns the current environment (can be None if not defined).

        :return: The current environment
        """
        return self.env

    def get_vec_normalize_env(self) -> Optional[VecNormalize]:
        """
        Return the ``VecNormalize`` wrapper of the training env
        if it exists.

        :return: The ``VecNormalize`` env.
        """
        return self._vec_normalize_env

    def set_env(self, env: GymEnv, force_reset: bool = True) -> None:
        """
        Checks the validity of the environment, and if it is coherent, set it as the current environment.
        Furthermore wrap any non vectorized env into a vectorized
        checked parameters:
        - observation_space
        - action_space

        :param env: The environment for learning a policy
        :param force_reset: Force call to ``reset()`` before training
            to avoid unexpected behavior.
            See issue https://github.com/DLR-RM/stable-baselines3/issues/597
        """
        # if it is not a VecEnv, make it a VecEnv
        # and do other transformations (dict obs, image transpose) if needed
        env = self._wrap_env(env, self.verbose)
        # Check that the observation spaces match
        check_for_correct_spaces(env, self.observation_space, self.action_space)
        # Update VecNormalize object
        # otherwise the wrong env may be used, see https://github.com/DLR-RM/stable-baselines3/issues/637
        self._vec_normalize_env = unwrap_vec_normalize(env)

        # Discard `_last_obs`, this will force the env to reset before training
        # See issue https://github.com/DLR-RM/stable-baselines3/issues/597
        if force_reset:
            self._last_obs = None

        self.n_envs = env.num_envs
        self.env = env

    @abstractmethod
    def learn(
        self,
        total_timesteps: int,
        callback: MaybeCallback = None,
        log_interval: int = 100,
        tb_log_name: str = "run",
        eval_env: Optional[GymEnv] = None,
        eval_freq: int = -1,
        n_eval_episodes: int = 5,
        eval_log_path: Optional[str] = None,
        reset_num_timesteps: bool = True,
    ) -> "BaseAlgorithm":
        """
        Return a trained model.

        :param total_timesteps: The total number of samples (env steps) to train on
        :param callback: callback(s) called at every step with state of the algorithm.
        :param log_interval: The number of timesteps before logging.
        :param tb_log_name: the name of the run for TensorBoard logging
        :param eval_env: Environment that will be used to evaluate the agent
        :param eval_freq: Evaluate the agent every ``eval_freq`` timesteps (this may vary a little)
        :param n_eval_episodes: Number of episode to evaluate the agent
        :param eval_log_path: Path to a folder where the evaluations will be saved
        :param reset_num_timesteps: whether or not to reset the current timestep number (used in logging)
        :return: the trained model
        """

    def predict(
        self,
        observation: np.ndarray,
        state: Optional[Tuple[np.ndarray, ...]] = None,
        episode_start: Optional[np.ndarray] = None,
        deterministic: bool = False,
    ) -> Tuple[np.ndarray, Optional[Tuple[np.ndarray, ...]]]:
        """
        Get the policy action from an observation (and optional hidden state).
        Includes sugar-coating to handle different observations (e.g. normalizing images).

        :param observation: the input observation
        :param state: The last hidden states (can be None, used in recurrent policies)
        :param episode_start: The last masks (can be None, used in recurrent policies)
            this correspond to beginning of episodes,
            where the hidden states of the RNN must be reset.
        :param deterministic: Whether or not to return deterministic actions.
        :return: the model's action and the next hidden state
            (used in recurrent policies)
        """
        return self.policy.predict(observation, state, episode_start, deterministic)

    def set_random_seed(self, seed: Optional[int] = None) -> None:
        """
        Set the seed of the pseudo-random generators
        (python, numpy, pytorch, gym, action_space)

        :param seed:
        """
        if seed is None:
            return
        set_random_seed(seed, using_cuda=self.device.type == th.device("cuda").type)
        self.action_space.seed(seed)
        if self.env is not None:
            self.env.seed(seed)
        if self.eval_env is not None:
            self.eval_env.seed(seed)

    def set_parameters(
        self,
        load_path_or_dict: Union[str, Dict[str, Dict]],
        exact_match: bool = True,
        device: Union[th.device, str] = "auto",
    ) -> None:
        """
        Load parameters from a given zip-file or a nested dictionary containing parameters for
        different modules (see ``get_parameters``).

        :param load_path_or_iter: Location of the saved data (path or file-like, see ``save``), or a nested
            dictionary containing nn.Module parameters used by the policy. The dictionary maps
            object names to a state-dictionary returned by ``torch.nn.Module.state_dict()``.
        :param exact_match: If True, the given parameters should include parameters for each
            module and each of their parameters, otherwise raises an Exception. If set to False, this
            can be used to update only specific parameters.
        :param device: Device on which the code should run.
        """
        params = None
        if isinstance(load_path_or_dict, dict):
            params = load_path_or_dict
        else:
            _, params, _ = load_from_zip_file(load_path_or_dict, device=device)

        # Keep track which objects were updated.
        # `_get_torch_save_params` returns [params, other_pytorch_variables].
        # We are only interested in former here.
        objects_needing_update = set(self._get_torch_save_params()[0])
        updated_objects = set()

        for name in params:
            attr = None
            try:
                attr = recursive_getattr(self, name)
            except Exception:
                # What errors recursive_getattr could throw? KeyError, but
                # possible something else too (e.g. if key is an int?).
                # Catch anything for now.
                raise ValueError(f"Key {name} is an invalid object name.")

            if isinstance(attr, th.optim.Optimizer):
                # Optimizers do not support "strict" keyword...
                # Seems like they will just replace the whole
                # optimizer state with the given one.
                # On top of this, optimizer state-dict
                # seems to change (e.g. first ``optim.step()``),
                # which makes comparing state dictionary keys
                # invalid (there is also a nesting of dictionaries
                # with lists with dictionaries with ...), adding to the
                # mess.
                #
                # TL;DR: We might not be able to reliably say
                # if given state-dict is missing keys.
                #
                # Solution: Just load the state-dict as is, and trust
                # the user has provided a sensible state dictionary.
                attr.load_state_dict(params[name])
            else:
                # Assume attr is th.nn.Module
                attr.load_state_dict(params[name], strict=exact_match)
            updated_objects.add(name)

        if exact_match and updated_objects != objects_needing_update:
            raise ValueError(
                "Names of parameters do not match agents' parameters: "
                f"expected {objects_needing_update}, got {updated_objects}"
            )

    @classmethod
    def load(
        cls,
        path: Union[str, pathlib.Path, io.BufferedIOBase],
        env: Optional[GymEnv] = None,
        device: Union[th.device, str] = "auto",
        custom_objects: Optional[Dict[str, Any]] = None,
        print_system_info: bool = False,
        force_reset: bool = True,
        **kwargs,
    ) -> "BaseAlgorithm":
        """
        Load the model from a zip-file.
        Warning: ``load`` re-creates the model from scratch, it does not update it in-place!
        For an in-place load use ``set_parameters`` instead.

        :param path: path to the file (or a file-like) where to
            load the agent from
        :param env: the new environment to run the loaded model on
            (can be None if you only need prediction from a trained model) has priority over any saved environment
        :param device: Device on which the code should run.
        :param custom_objects: Dictionary of objects to replace
            upon loading. If a variable is present in this dictionary as a
            key, it will not be deserialized and the corresponding item
            will be used instead. Similar to custom_objects in
            ``keras.models.load_model``. Useful when you have an object in
            file that can not be deserialized.
        :param print_system_info: Whether to print system info from the saved model
            and the current system info (useful to debug loading issues)
        :param force_reset: Force call to ``reset()`` before training
            to avoid unexpected behavior.
            See https://github.com/DLR-RM/stable-baselines3/issues/597
        :param kwargs: extra arguments to change the model when loading
        :return: new model instance with loaded parameters
        """
        if print_system_info:
            print("== CURRENT SYSTEM INFO ==")
            get_system_info()

        data, params, pytorch_variables = load_from_zip_file(
            path, device=device, custom_objects=custom_objects, print_system_info=print_system_info
        )

        # Remove stored device information and replace with ours
        if "policy_kwargs" in data:
            if "device" in data["policy_kwargs"]:
                del data["policy_kwargs"]["device"]

        if "policy_kwargs" in kwargs and kwargs["policy_kwargs"] != data["policy_kwargs"]:
            raise ValueError(
                f"The specified policy kwargs do not equal the stored policy kwargs."
                f"Stored kwargs: {data['policy_kwargs']}, specified kwargs: {kwargs['policy_kwargs']}"
            )

        if "observation_space" not in data or "action_space" not in data:
            raise KeyError("The observation_space and action_space were not given, can't verify new environments")

        if env is not None:
            # Wrap first if needed
            env = cls._wrap_env(env, data["verbose"])
            # Check if given env is valid
            check_for_correct_spaces(env, data["observation_space"], data["action_space"])
            # Discard `_last_obs`, this will force the env to reset before training
            # See issue https://github.com/DLR-RM/stable-baselines3/issues/597
            if force_reset and data is not None:
                data["_last_obs"] = None
        else:
            # Use stored env, if one exists. If not, continue as is (can be used for predict)
            if "env" in data:
                env = data["env"]

        # noinspection PyArgumentList
        model = cls(  # pytype: disable=not-instantiable,wrong-keyword-args
            policy=data["policy_class"],
            env=env,
            device=device,
            _init_setup_model=False,  # pytype: disable=not-instantiable,wrong-keyword-args
        )

        # load parameters
        model.__dict__.update(data)
        model.__dict__.update(kwargs)
        model._setup_model()

        # put state_dicts back in place
        model.set_parameters(params, exact_match=True, device=device)

        # put other pytorch variables back in place
        if pytorch_variables is not None:
            for name in pytorch_variables:
                # Skip if PyTorch variable was not defined (to ensure backward compatibility).
                # This happens when using SAC/TQC.
                # SAC has an entropy coefficient which can be fixed or optimized.
                # If it is optimized, an additional PyTorch variable `log_ent_coef` is defined,
                # otherwise it is initialized to `None`.
                if pytorch_variables[name] is None:
                    continue
                # Set the data attribute directly to avoid issue when using optimizers
                # See https://github.com/DLR-RM/stable-baselines3/issues/391
                recursive_setattr(model, name + ".data", pytorch_variables[name].data)

        # Sample gSDE exploration matrix, so it uses the right device
        # see issue #44
        if model.use_sde:
            model.policy.reset_noise()  # pytype: disable=attribute-error
        return model

    def get_parameters(self) -> Dict[str, Dict]:
        """
        Return the parameters of the agent. This includes parameters from different networks, e.g.
        critics (value functions) and policies (pi functions).

        :return: Mapping of from names of the objects to PyTorch state-dicts.
        """
        state_dicts_names, _ = self._get_torch_save_params()
        params = {}
        for name in state_dicts_names:
            attr = recursive_getattr(self, name)
            # Retrieve state dict
            params[name] = attr.state_dict()
        return params

    def save(
        self,
        path: Union[str, pathlib.Path, io.BufferedIOBase],
        exclude: Optional[Iterable[str]] = None,
        include: Optional[Iterable[str]] = None,
    ) -> None:
        """
        Save all the attributes of the object and the model parameters in a zip-file.

        :param path: path to the file where the rl agent should be saved
        :param exclude: name of parameters that should be excluded in addition to the default ones
        :param include: name of parameters that might be excluded but should be included anyway
        """
        # Copy parameter list so we don't mutate the original dict
        data = self.__dict__.copy()

        # Exclude is union of specified parameters (if any) and standard exclusions
        if exclude is None:
            exclude = []
        exclude = set(exclude).union(self._excluded_save_params())

        # Do not exclude params if they are specifically included
        if include is not None:
            exclude = exclude.difference(include)

        state_dicts_names, torch_variable_names = self._get_torch_save_params()
        all_pytorch_variables = state_dicts_names + torch_variable_names
        for torch_var in all_pytorch_variables:
            # We need to get only the name of the top most module as we'll remove that
            var_name = torch_var.split(".")[0]
            # Any params that are in the save vars must not be saved by data
            exclude.add(var_name)

        # Remove parameter entries of parameters which are to be excluded
        for param_name in exclude:
            data.pop(param_name, None)

        # Build dict of torch variables
        pytorch_variables = None
        if torch_variable_names is not None:
            pytorch_variables = {}
            for name in torch_variable_names:
                attr = recursive_getattr(self, name)
                pytorch_variables[name] = attr

        # Build dict of state_dicts
        params_to_save = self.get_parameters()

        save_to_zip_file(path, data=data, params=params_to_save, pytorch_variables=pytorch_variables)