collectors.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import abc
import inspect
import os
import queue
import time
from collections import OrderedDict
from copy import deepcopy
from multiprocessing import connection, queues
from textwrap import indent
from typing import Callable, Iterator, Optional, Sequence, Tuple, Union, Any, Dict

import numpy as np
import torch
import torch.nn as nn
from torch import multiprocessing as mp
from torch.utils.data import IterableDataset

from torchrl.envs.utils import set_exploration_mode, step_mdp
from .._utils import _check_for_faulty_process, prod
from ..modules.tensordict_module import ProbabilisticTensorDictModule, TensorDictModule
from .utils import split_trajectories

__all__ = [
    "SyncDataCollector",
    "aSyncDataCollector",
    "MultiaSyncDataCollector",
    "MultiSyncDataCollector",
]

from torchrl.envs.transforms import TransformedEnv
from ..data import TensorSpec
from ..data.tensordict.tensordict import TensorDictBase, TensorDict
from ..data.utils import CloudpickleWrapper, DEVICE_TYPING
from ..envs.common import EnvBase
from ..envs.vec_env import _BatchedEnv

_TIMEOUT = 1.0
_MIN_TIMEOUT = 1e-3  # should be several orders of magnitude inferior wrt time spent collecting a trajectory
_MAX_IDLE_COUNT = int(os.environ.get("MAX_IDLE_COUNT", 1000))

DEFAULT_EXPLORATION_MODE: str = "random"


class RandomPolicy:
    def __init__(self, action_spec: TensorSpec):
        """Random policy for a given action_spec.

        This is a wrapper around the action_spec.rand method.


        $ python example_google.py

        Args:
            action_spec: TensorSpec object describing the action specs

        Examples:
            >>> from torchrl.data.tensor_specs import NdBoundedTensorSpec
            >>> from torchrl.data.tensordict import TensorDict
            >>> action_spec = NdBoundedTensorSpec(-torch.ones(3), torch.ones(3))
            >>> actor = RandomPolicy(spec=action_spec)
            >>> td = actor(TensorDict(batch_size=[])) # selects a random action in the cube [-1; 1]

        """
        self.action_spec = action_spec

    def __call__(self, td: TensorDictBase) -> TensorDictBase:
        return td.set("action", self.action_spec.rand(td.batch_size))


def recursive_map_to_cpu(dictionary: OrderedDict) -> OrderedDict:
    return OrderedDict(
        **{
            k: recursive_map_to_cpu(item)
            if isinstance(item, OrderedDict)
            else item.cpu()
            if isinstance(item, torch.Tensor)
            else item
            for k, item in dictionary.items()
        }
    )


def _policy_is_tensordict_compatible(policy: nn.Module):
    sig = inspect.signature(policy.forward)

    if isinstance(policy, TensorDictModule) or (
        len(sig.parameters) == 1
        and hasattr(policy, "in_keys")
        and hasattr(policy, "out_keys")
    ):
        # if the policy is a TensorDictModule or takes a single argument and defines
        # in_keys and out_keys then we assume it can already deal with TensorDict input
        # to forward and we return True
        return True
    elif not hasattr(policy, "in_keys") and not hasattr(policy, "out_keys"):
        # if it's not a TensorDictModule, and in_keys and out_keys are not defined then
        # we assume no TensorDict compatibility and will try to wrap it.
        return False

    # if in_keys or out_keys were defined but policy is not a TensorDictModule or
    # accepts multiple arguments then it's likely the user is trying to do something
    # that will have undetermined behaviour, we raise an error
    raise TypeError(
        "Received a policy that defines in_keys or out_keys and also expects multiple "
        "arguments to policy.forward. If the policy is compatible with TensorDict, it "
        "should take a single argument of type TensorDict to policy.forward and define "
        "both in_keys and out_keys. Alternatively, policy.forward can accept "
        "arbitrarily many tensor inputs and leave in_keys and out_keys undefined and "
        "TorchRL will attempt to automatically wrap the policy with a TensorDictModule."
    )


class _DataCollector(IterableDataset, metaclass=abc.ABCMeta):
    def _get_policy_and_device(
        self,
        policy: Optional[
            Union[
                ProbabilisticTensorDictModule,
                Callable[[TensorDictBase], TensorDictBase],
            ]
        ] = None,
        device: Optional[DEVICE_TYPING] = None,
        observation_spec: TensorSpec = None,
    ) -> Tuple[
        ProbabilisticTensorDictModule, torch.device, Union[None, Callable[[], dict]]
    ]:
        """Util method to get a policy and its device given the collector __init__ inputs.

        From a policy and a device, assigns the self.device attribute to
        the desired device and maps the policy onto it or (if the device is
        ommitted) assigns the self.device attribute to the policy device.

        Args:
            create_env_fn (Callable or list of callables): an env creator
                function (or a list of creators)
            create_env_kwargs (dictionary): kwargs for the env creator
            policy (ProbabilisticTensorDictModule, optional): a policy to be used
            device (int, str or torch.device, optional): device where to place
                the policy
            observation_spec (TensorSpec, optional): spec of the observations

        """
        # if create_env_fn is not None:
        #     if create_env_kwargs is None:
        #         create_env_kwargs = dict()
        #     self.create_env_fn = create_env_fn
        #     if isinstance(create_env_fn, EnvBase):
        #         env = create_env_fn
        #     else:
        #         env = self.create_env_fn(**create_env_kwargs)
        # else:
        #     env = None

        if policy is None:
            if not hasattr(self, "env") or self.env is None:
                raise ValueError(
                    "env must be provided to _get_policy_and_device if policy is None"
                )
            policy = RandomPolicy(self.env.action_spec)
        elif isinstance(policy, nn.Module):
            # TODO: revisit these checks when we have determined whether arbitrary
            # callables should be supported as policies.
            if not _policy_is_tensordict_compatible(policy):
                # policy is a nn.Module that doesn't operate on tensordicts directly
                # so we attempt to auto-wrap policy with TensorDictModule
                if observation_spec is None:
                    raise ValueError(
                        "Unable to read observation_spec from the environment. This is "
                        "required to check compatibility of the environment and policy "
                        "since the policy is a nn.Module that operates on tensors "
                        "rather than a TensorDictModule or a nn.Module that accepts a "
                        "TensorDict as input and defines in_keys and out_keys."
                    )
                sig = inspect.signature(policy.forward)
                next_observation = {
                    key[5:]: value
                    for key, value in observation_spec.rand().items()
                    if key.startswith("next_")
                }
                if set(sig.parameters) == set(next_observation):
                    out_keys = ["action"]
                    output = policy(**next_observation)

                    if isinstance(output, tuple):
                        out_keys.extend(f"output{i+1}" for i in range(len(output) - 1))

                    policy = TensorDictModule(
                        policy, in_keys=list(sig.parameters), out_keys=out_keys
                    )
                else:
                    raise TypeError(
                        "Arguments to policy.forward are incompatible with entries in "
                        "env.observation_spec. If you want TorchRL to automatically "
                        "wrap your policy with a TensorDictModule then the arguments "
                        "to policy.forward must correspond one-to-one with entries in "
                        "env.observation_spec that are prefixed with 'next_'. For more "
                        "complex behaviour and more control you can consider writing "
                        "your own TensorDictModule."
                    )

        try:
            policy_device = next(policy.parameters()).device
        except:  # noqa
            policy_device = (
                torch.device(device) if device is not None else torch.device("cpu")
            )

        device = torch.device(device) if device is not None else policy_device
        if device is None:
            device = torch.device("cpu")
        get_weights_fn = None
        if policy_device != device:
            get_weights_fn = policy.state_dict
            policy = deepcopy(policy).requires_grad_(False).to(device)
            if device == torch.device("cpu"):
                policy.share_memory()
        return policy, device, get_weights_fn

    def update_policy_weights_(self) -> None:
        """Update the policy weights if the policy of the data collector and the trained policy live on different devices."""
        if self.get_weights_fn is not None:
            self.policy.load_state_dict(self.get_weights_fn())

    def __iter__(self) -> Iterator[TensorDictBase]:
        return self.iterator()

    @abc.abstractmethod
    def iterator(self) -> Iterator[TensorDictBase]:
        raise NotImplementedError

    @abc.abstractmethod
    def set_seed(self, seed: int, static_seed: bool = False) -> int:
        raise NotImplementedError

    @abc.abstractmethod
    def state_dict(self) -> OrderedDict:
        raise NotImplementedError

    @abc.abstractmethod
    def load_state_dict(self, state_dict: OrderedDict) -> None:
        raise NotImplementedError

    def __repr__(self) -> str:
        string = f"{self.__class__.__name__}()"
        return string


class SyncDataCollector(_DataCollector):
    """Generic data collector for RL problems. Requires and environment constructor and a policy.

    Args:
        create_env_fn (Callable), returns an instance of EnvBase class.
        policy (Callable, optional): Policy to be executed in the environment.
            Must accept TensorDictBase object as input.
        total_frames (int): lower bound of the total number of frames returned by the collector. The iterator will
            stop once the total number of frames equates or exceeds the total number of frames passed to the
            collector.
        create_env_kwargs (dict, optional): Dictionary of kwargs for create_env_fn.
        max_frames_per_traj (int, optional): Maximum steps per trajectory. Note that a trajectory can span over multiple batches
            (unless reset_at_each_iter is set to True, see below). Once a trajectory reaches n_steps_max,
            the environment is reset. If the environment wraps multiple environments together, the number of steps
            is tracked for each environment independently. Negative values are allowed, in which case this argument
            is ignored.
            default: -1 (i.e. no maximum number of steps)
        frames_per_batch (int): Time-length of a batch.
            reset_at_each_iter and frames_per_batch == n_steps_max are equivalent configurations.
            default: 200
        init_random_frames (int, optional): Number of frames for which the policy is ignored before it is called.
            This feature is mainly intended to be used in offline/model-based settings, where a batch of random
            trajectories can be used to initialize training.
            default=-1 (i.e. no random frames)
        reset_at_each_iter (bool): Whether or not environments should be reset for each batch.
            default=False.
        postproc (Callable, optional): A Batcher is an object that will read a batch of data and return it in a useful format for training.
            default: None.
        split_trajs (bool): Boolean indicating whether the resulting TensorDict should be split according to the trajectories.
            See utils.split_trajectories for more information.
        device (int, str or torch.device, optional): The device on which the policy will be placed.
            If it differs from the input policy device, the update_policy_weights_() method should be queried
            at appropriate times during the training loop to accommodate for the lag between parameter configuration
            at various times.
            default = None (i.e. policy is kept on its original device)
        seed (int, optional): seed to be used for torch and numpy.
        pin_memory (bool): whether pin_memory() should be called on the outputs.
        passing_device (int, str or torch.device, optional): The device on which the output TensorDict will be stored.
            For long trajectories, it may be necessary to store the data on a different device than the one where
            the policy is stored.
            default = None
        return_in_place (bool): if True, the collector will yield the same tensordict container with updated values
            at each iteration.
            default = False
        exploration_mode (str, optional): interaction mode to be used when collecting data. Must be one of "random",
            "mode" or "mean".
            default = "random"
        init_with_lag (bool, optional): if True, the first trajectory will be truncated earlier at a random step.
            This is helpful to desynchronize the environments, such that steps do no match in all collected rollouts.
            default = True
        return_same_td (bool, optional): if True, the same TensorDict will be returned at each iteration, with its values
            updated. This feature should be used cautiously: if the same tensordict is added to a replay buffer for instance,
            the whole content of the buffer will be identical.
            Default is False.
    """

    def __init__(
        self,
        create_env_fn: Union[
            EnvBase, "EnvCreator", Sequence[Callable[[], EnvBase]]  # noqa: F821
        ],  # noqa: F821
        policy: Optional[
            Union[
                ProbabilisticTensorDictModule,
                Callable[[TensorDictBase], TensorDictBase],
            ]
        ] = None,
        total_frames: Optional[int] = -1,
        create_env_kwargs: Optional[dict] = None,
        max_frames_per_traj: int = -1,
        frames_per_batch: int = 200,
        init_random_frames: int = -1,
        reset_at_each_iter: bool = False,
        postproc: Optional[Callable[[TensorDictBase], TensorDictBase]] = None,
        split_trajs: Optional[bool] = None,
        device: DEVICE_TYPING = None,
        passing_device: DEVICE_TYPING = None,
        seed: Optional[int] = None,
        pin_memory: bool = False,
        return_in_place: bool = False,
        exploration_mode: str = DEFAULT_EXPLORATION_MODE,
        init_with_lag: bool = False,
        return_same_td: bool = False,
        reset_when_done: bool = True,
    ):
        self.closed = True
        if seed is not None:
            torch.manual_seed(seed)
            np.random.seed(seed)

        if create_env_kwargs is None:
            create_env_kwargs = {}
        if not isinstance(create_env_fn, EnvBase):
            env = create_env_fn(**create_env_kwargs)
        else:
            env = create_env_fn
            if create_env_kwargs:
                if not isinstance(env, _BatchedEnv):
                    raise RuntimeError(
                        "kwargs were passed to SyncDataCollector but they can't be set "
                        f"on environment of type {type(create_env_fn)}."
                    )
                env.update_kwargs(create_env_kwargs)

        if passing_device is None:
            if device is not None:
                passing_device = device
            elif policy is not None:
                try:
                    policy_device = next(policy.parameters()).device
                except (AttributeError, StopIteration):
                    policy_device = torch.device("cpu")
                passing_device = policy_device
            else:
                passing_device = torch.device("cpu")

        self.passing_device = torch.device(passing_device)
        self.env: EnvBase = env.to(self.passing_device)
        self.closed = False
        self.reset_when_done = reset_when_done
        self.n_env = self.env.numel()

        (self.policy, self.device, self.get_weights_fn,) = self._get_policy_and_device(
            policy=policy,
            device=device,
            observation_spec=self.env.observation_spec,
        )

        self.env_device = env.device
        if not total_frames > 0:
            total_frames = float("inf")
        self.total_frames = total_frames
        self.reset_at_each_iter = reset_at_each_iter
        self.init_random_frames = init_random_frames
        self.postproc = postproc
        if self.postproc is not None:
            self.postproc.to(self.passing_device)
        self.max_frames_per_traj = max_frames_per_traj
        self.frames_per_batch = -(-frames_per_batch // self.n_env)
        self.pin_memory = pin_memory
        self.exploration_mode = (
            exploration_mode if exploration_mode else DEFAULT_EXPLORATION_MODE
        )
        self.init_with_lag = init_with_lag and max_frames_per_traj > 0
        self.return_same_td = return_same_td

        self._tensordict = env.reset()
        self._tensordict.set(
            "step_count", torch.zeros(*self.env.batch_size, 1, dtype=torch.int)
        )

        if (
            hasattr(self.policy, "spec")
            and self.policy.spec is not None
            and all(v is not None for v in self.policy.spec.values())
            and set(self.policy.spec.keys()) == set(self.policy.out_keys)
        ):
            # if policy spec is non-empty, all the values are not None and the keys
            # match the out_keys we assume the user has given all relevant information
            self._tensordict_out = TensorDict(
                {
                    **env.observation_spec.zero(env.batch_size),
                    "reward": env.reward_spec.zero(env.batch_size),
                    "done": torch.zeros(
                        env.batch_size, dtype=torch.bool, device=env.device
                    ),
                    **self.policy.spec.zero(env.batch_size),
                },
                env.batch_size,
                device=env.device,
            )
            self._tensordict_out = (
                self._tensordict_out.unsqueeze(-1)
                .expand(*env.batch_size, self.frames_per_batch)
                .to_tensordict()
            )
            self._tensordict_out = self._tensordict_out.update(
                step_mdp(self._tensordict_out)
            )  # add "observation" when there is "next_observation"
        else:
            # otherwise, we perform a small number of steps with the policy to
            # determine the relevant keys with which to pre-populate _tensordict_out.
            # See #505 for additional context.
            self._tensordict_out = self.env.rollout(
                3, self.policy, auto_cast_to_device=True
            )
            if env.batch_size:
                self._tensordict_out = self._tensordict_out[..., :1]
            else:
                self._tensordict_out = self._tensordict_out[:1]
            self._tensordict_out = (
                self._tensordict_out.expand(*env.batch_size, self.frames_per_batch)
                .to_tensordict()
                .zero_()
                .detach()
            )
            env.reset()

        # in addition to outputs of the policy, we add traj_ids and step_count to
        # _tensordict_out which will be collected during rollout
        if len(self.env.batch_size):
            traj_ids = torch.zeros(*self._tensordict_out.batch_size, 1)
        else:
            traj_ids = torch.zeros(*self._tensordict_out.batch_size, 1, 1)

        self._tensordict_out.set("traj_ids", traj_ids)
        self._tensordict_out.set(
            "step_count", torch.zeros(*self._tensordict_out.batch_size, 1)
        )

        self.return_in_place = return_in_place
        if split_trajs is None:
            if not self.reset_when_done:
                split_trajs = False
            else:
                split_trajs = True
        elif not self.reset_when_done and split_trajs:
            raise RuntimeError(
                "Cannot split trajectories when reset_when_done is False."
            )
        self.split_trajs = split_trajs
        if self.return_in_place and self.split_trajs:
            raise RuntimeError(
                "the 'return_in_place' and 'split_trajs' argument are incompatible, but found to be both "
                "True. split_trajs=True will cause the output tensordict to have an unpredictable output "
                "shape, which prevents caching and overwriting the tensors."
            )
        self._td_env = None
        self._td_policy = None
        self._has_been_done = None
        self._exclude_private_keys = True

    def set_seed(self, seed: int, static_seed: bool = False) -> int:
        """Sets the seeds of the environments stored in the DataCollector.

        Args:
            seed (int): integer representing the seed to be used for the environment.
            static_seed(bool, optional): if True, the seed is not incremented.
                Defaults to False

        Returns:
            Output seed. This is useful when more than one environment is contained in the DataCollector, as the
            seed will be incremented for each of these. The resulting seed is the seed of the last environment.

        Examples:
            >>> env_fn = lambda: GymEnv("Pendulum-v1")
            >>> env_fn_parallel = lambda: ParallelEnv(6, env_fn)
            >>> collector = SyncDataCollector(env_fn_parallel)
            >>> out_seed = collector.set_seed(1)  # out_seed = 6

        """
        return self.env.set_seed(seed, static_seed=static_seed)

    def iterator(self) -> Iterator[TensorDictBase]:
        """Iterates through the DataCollector.

        Yields: TensorDictBase objects containing (chunks of) trajectories

        """
        total_frames = self.total_frames
        i = -1
        self._frames = 0
        while True:
            i += 1
            self._iter = i
            tensordict_out = self.rollout()
            self._frames += tensordict_out.numel()
            if self._frames >= total_frames:
                self.env.close()

            if self.split_trajs:
                tensordict_out = split_trajectories(tensordict_out)
            if self.postproc is not None:
                tensordict_out = self.postproc(tensordict_out)
            if self._exclude_private_keys:
                excluded_keys = [
                    key for key in tensordict_out.keys() if key.startswith("_")
                ]
                tensordict_out = tensordict_out.exclude(*excluded_keys, inplace=True)
            if self.return_same_td:
                yield tensordict_out
            else:
                yield tensordict_out.clone()

            del tensordict_out
            if self._frames >= self.total_frames:
                break

    def _cast_to_policy(self, td: TensorDictBase) -> TensorDictBase:
        policy_device = self.device
        if hasattr(self.policy, "in_keys"):
            td = td.select(*self.policy.in_keys)
        if self._td_policy is None:
            self._td_policy = td.to(policy_device)
        else:
            if td.device == torch.device("cpu") and self.pin_memory:
                td.pin_memory()
            self._td_policy.update(td, inplace=True)
        return self._td_policy

    def _cast_to_env(
        self, td: TensorDictBase, dest: Optional[TensorDictBase] = None
    ) -> TensorDictBase:
        env_device = self.env_device
        if dest is None:
            if self._td_env is None:
                self._td_env = td.to(env_device)
            else:
                self._td_env.update(td, inplace=True)
            return self._td_env
        else:
            return dest.update(td, inplace=True)

    def _reset_if_necessary(self) -> None:
        done = self._tensordict.get("done")
        if not self.reset_when_done:
            done = torch.zeros_like(done)
        steps = self._tensordict.get("step_count")
        done_or_terminated = done | (steps == self.max_frames_per_traj)
        if self._has_been_done is None:
            self._has_been_done = done_or_terminated
        else:
            self._has_been_done = self._has_been_done | done_or_terminated
        if not self._has_been_done.all() and self.init_with_lag:
            _reset = torch.zeros_like(done_or_terminated).bernoulli_(
                1 / self.max_frames_per_traj
            )
            _reset[self._has_been_done] = False
            done_or_terminated = done_or_terminated | _reset
        if done_or_terminated.any():
            traj_ids = self._tensordict.get("traj_ids").clone()
            steps = steps.clone()
            if len(self.env.batch_size):
                self._tensordict.masked_fill_(done_or_terminated.squeeze(-1), 0)
                self._tensordict.set("reset_workers", done_or_terminated)
            else:
                self._tensordict.zero_()

            self.env.reset(self._tensordict)
            if self._tensordict.get("done").any():
                raise RuntimeError(
                    f"Got {sum(self._tensordict.get('done'))} done envs after reset."
                )
            if len(self.env.batch_size):
                self._tensordict.del_("reset_workers")
            traj_ids[done_or_terminated] = traj_ids.max() + torch.arange(
                1, done_or_terminated.sum() + 1, device=traj_ids.device
            )
            steps[done_or_terminated] = 0
            self._tensordict.set("traj_ids", traj_ids)  # no ops if they already match
            self._tensordict.set("step_count", steps)

    @torch.no_grad()
    def rollout(self) -> TensorDictBase:
        """Computes a rollout in the environment using the provided policy.

        Returns:
            TensorDictBase containing the computed rollout.

        """
        if self.reset_at_each_iter:
            self._tensordict.update(self.env.reset(), inplace=True)
            self._tensordict.fill_("step_count", 0)

        n = self.env.batch_size[0] if len(self.env.batch_size) else 1
        self._tensordict.set("traj_ids", torch.arange(n).unsqueeze(-1))

        tensordict_out = []
        with set_exploration_mode(self.exploration_mode):
            for _ in range(self.frames_per_batch):
                if self._frames < self.init_random_frames:
                    self.env.rand_step(self._tensordict)
                else:
                    td_cast = self._cast_to_policy(self._tensordict)
                    td_cast = self.policy(td_cast)
                    self._cast_to_env(td_cast, self._tensordict)
                    self.env.step(self._tensordict)

                step_count = self._tensordict.get("step_count")
                step_count += 1
                tensordict_out.append(self._tensordict.clone())

                self._reset_if_necessary()
                self._tensordict.update(
                    step_mdp(
                        self._tensordict.exclude("reward", "done"), keep_other=True
                    ),
                    inplace=True,
                )
            if self.return_in_place and len(self._tensordict_out.keys()) > 0:
                tensordict_out = torch.stack(tensordict_out, len(self.env.batch_size))
                tensordict_out = tensordict_out.select(*self._tensordict_out.keys())
                return self._tensordict_out.update_(tensordict_out)
        return torch.stack(
            tensordict_out,
            len(self.env.batch_size),
            out=self._tensordict_out,
        )  # dim 0 for single env, dim 1 for batch

    def reset(self, index=None, **kwargs) -> None:
        """Resets the environments to a new initial state."""
        if index is not None:
            # check that the env supports partial reset
            if prod(self.env.batch_size) == 0:
                raise RuntimeError("resetting unique env with index is not permitted.")
            reset_workers = torch.zeros(
                *self.env.batch_size,
                1,
                dtype=torch.bool,
                device=self.env.device,
            )
            reset_workers[index] = 1
            td_in = TensorDict({"reset_workers": reset_workers}, self.env.batch_size)
            self._tensordict[index].zero_()
        else:
            td_in = None
            self._tensordict.zero_()

        if td_in:
            self._tensordict.update(td_in, inplace=True)

        self._tensordict.update(self.env.reset(**kwargs), inplace=True)
        self._tensordict.fill_("step_count", 0)

    def shutdown(self) -> None:
        """Shuts down all workers and/or closes the local environment."""
        if not self.closed:
            self.closed = True
            del self._tensordict, self._tensordict_out
            if not self.env.is_closed:
                self.env.close()
            del self.env

    def __del__(self):
        self.shutdown()  # make sure env is closed

    def state_dict(self) -> OrderedDict:
        """Returns the local state_dict of the data collector (environment and policy).

        Returns:
            an ordered dictionary with fields :obj:`"policy_state_dict"` and
            `"env_state_dict"`.

        """
        if isinstance(self.env, TransformedEnv):
            env_state_dict = self.env.transform.state_dict()
        elif isinstance(self.env, _BatchedEnv):
            env_state_dict = self.env.state_dict()
        else:
            env_state_dict = OrderedDict()

        if hasattr(self.policy, "state_dict"):
            policy_state_dict = self.policy.state_dict()
            state_dict = OrderedDict(
                policy_state_dict=policy_state_dict,
                env_state_dict=env_state_dict,
            )
        else:
            state_dict = OrderedDict(env_state_dict=env_state_dict)

        return state_dict

    def load_state_dict(self, state_dict: OrderedDict, **kwargs) -> None:
        """Loads a state_dict on the environment and policy.

        Args:
            state_dict (OrderedDict): ordered dictionary containing the fields
                `"policy_state_dict"` and :obj:`"env_state_dict"`.

        """
        strict = kwargs.get("strict", True)
        if strict or "env_state_dict" in state_dict:
            self.env.load_state_dict(state_dict["env_state_dict"], **kwargs)
        if strict or "policy_state_dict" in state_dict:
            self.policy.load_state_dict(state_dict["policy_state_dict"], **kwargs)

    def __repr__(self) -> str:
        env_str = indent(f"env={self.env}", 4 * " ")
        policy_str = indent(f"policy={self.policy}", 4 * " ")
        td_out_str = indent(f"td_out={self._tensordict_out}", 4 * " ")
        string = (
            f"{self.__class__.__name__}("
            f"\n{env_str},"
            f"\n{policy_str},"
            f"\n{td_out_str},"
            f"\nexploration={self.exploration_mode})"
        )
        return string


class _MultiDataCollector(_DataCollector):
    """Runs a given number of DataCollectors on separate processes.

    Args:
        create_env_fn (list of Callabled): list of Callables, each returning an instance of EnvBase
        policy (Callable, optional): Instance of ProbabilisticTensorDictModule class.
            Must accept TensorDictBase object as input.
        total_frames (int): lower bound of the total number of frames returned by the collector. In parallel settings,
            the actual number of frames may well be greater than this as the closing signals are sent to the
            workers only once the total number of frames has been collected on the server.
        create_env_kwargs (dict, optional): A (list of) dictionaries with the arguments used to create an environment
        max_frames_per_traj: Maximum steps per trajectory. Note that a trajectory can span over multiple batches
            (unless reset_at_each_iter is set to True, see below). Once a trajectory reaches n_steps_max,
            the environment is reset. If the environment wraps multiple environments together, the number of steps
            is tracked for each environment independently. Negative values are allowed, in which case this argument
            is ignored.
            default: -1 (i.e. no maximum number of steps)
        frames_per_batch (int): Time-length of a batch.
            reset_at_each_iter and frames_per_batch == n_steps_max are equivalent configurations.
            default: 200
        init_random_frames (int): Number of frames for which the policy is ignored before it is called.
            This feature is mainly intended to be used in offline/model-based settings, where a batch of random
            trajectories can be used to initialize training.
            default=-1 (i.e. no random frames)
        reset_at_each_iter (bool): Whether or not environments should be reset for each batch.
            default=False.
        postproc (callable, optional): A PostProcessor is an object that will read a batch of data and process it in a
            useful format for training.
            default: None.
        split_trajs (bool): Boolean indicating whether the resulting TensorDict should be split according to the trajectories.
            See utils.split_trajectories for more information.
        devices (int, str, torch.device or sequence of such, optional): The devices on which the policy will be placed.
            If it differs from the input policy device, the update_policy_weights_() method should be queried
            at appropriate times during the training loop to accommodate for the lag between parameter configuration
            at various times.
            default = None (i.e. policy is kept on its original device)
        passing_devices (int, str, torch.device or sequence of such, optional): The devices on which the output
            TensorDict will be stored. For long trajectories, it may be necessary to store the data on a different
            device than the one where the policy is stored.
            default = None
        update_at_each_batch (bool): if True, the policy weights will be updated every time a batch of trajectories
            is collected.
            default=False
        init_with_lag (bool, optional): if True, the first trajectory will be truncated earlier at a random step.
            This is helpful to desynchronize the environments, such that steps do no match in all collected rollouts.
            default = True
       exploration_mode (str, optional): interaction mode to be used when collecting data. Must be one of "random",
            "mode" or "mean".
            default = "random"
        reset_when_done (bool, optional): if True, the contained environment will be reset
            every time it hits a done. If the env contains multiple independent envs, a
            reset index will be passed to it to reset only thos environments that need to
            be reset. In practice, this will happen through a call to :obj:`env.reset(tensordict)`,
            in other words, if the env is a multi-agent env, all agents will be
            reset once one of them is done.
            Defaults to `True`.

    """

    def __init__(
        self,
        create_env_fn: Sequence[Callable[[], EnvBase]],
        policy: Optional[
            Union[
                ProbabilisticTensorDictModule,
                Callable[[TensorDictBase], TensorDictBase],
            ]
        ] = None,
        total_frames: Optional[int] = -1,
        create_env_kwargs: Optional[Sequence[dict]] = None,
        max_frames_per_traj: int = -1,
        frames_per_batch: int = 200,
        init_random_frames: int = -1,
        reset_at_each_iter: bool = False,
        postproc: Optional[Callable[[TensorDictBase], TensorDictBase]] = None,
        split_trajs: Optional[bool] = None,
        devices: DEVICE_TYPING = None,
        seed: Optional[int] = None,
        pin_memory: bool = False,
        passing_devices: Optional[Union[DEVICE_TYPING, Sequence[DEVICE_TYPING]]] = None,
        update_at_each_batch: bool = False,
        init_with_lag: bool = False,
        exploration_mode: str = DEFAULT_EXPLORATION_MODE,
        reset_when_done: bool = True,
    ):
        self.closed = True
        self.create_env_fn = create_env_fn
        self.num_workers = len(create_env_fn)
        self.create_env_kwargs = (
            create_env_kwargs
            if create_env_kwargs is not None
            else [{} for _ in range(self.num_workers)]
        )
        # Preparing devices:
        # We want the user to be able to choose, for each worker, on which
        # device will the policy live and which device will be used to store
        # data. Those devices may or may not match.
        # One caveat is that, if there is only one device for the policy, and
        # if there are multiple workers, sending the same device and policy
        # to be copied to each worker will result in multiple copies of the
        # same policy on the same device.
        # To go around this, we do the copies of the policy in the server
        # (this object) to each possible device, and send to all the
        # processes their copy of the policy.

        def device_err_msg(device_name, devices_list):
            return (
                f"The length of the {device_name} argument should match the "
                f"number of workers of the collector. Got len("
                f"create_env_fn)={self.num_workers} and len("
                f"passing_devices)={len(devices_list)}"
            )

        if isinstance(devices, (str, int, torch.device)):
            devices = [torch.device(devices) for _ in range(self.num_workers)]
        elif devices is None:
            devices = [None for _ in range(self.num_workers)]
        elif isinstance(devices, Sequence):
            if len(devices) != self.num_workers:
                raise RuntimeError(device_err_msg("devices", devices))
            devices = [torch.device(_device) for _device in devices]
        else:
            raise ValueError(
                "devices should be either None, a torch.device or equivalent "
                "or an iterable of devices. "
                f"Found {type(devices)} instead."
            )
        self._policy_dict = {}
        self._get_weights_fn_dict = {}

        for i, (_device, create_env, kwargs) in enumerate(
            zip(devices, self.create_env_fn, self.create_env_kwargs)
        ):
            if _device in self._policy_dict:
                devices[i] = _device
                continue

            if hasattr(create_env, "observation_spec"):
                observation_spec = create_env.observation_spec
            else:
                try:
                    observation_spec = create_env(**kwargs).observation_spec
                except:  # noqa
                    observation_spec = None

            _policy, _device, _get_weight_fn = self._get_policy_and_device(
                policy=policy, device=_device, observation_spec=observation_spec
            )
            self._policy_dict[_device] = _policy
            self._get_weights_fn_dict[_device] = _get_weight_fn
            devices[i] = _device
        self.devices = devices

        if passing_devices is None:
            self.passing_devices = self.devices
        else:
            if isinstance(passing_devices, (str, int, torch.device)):
                self.passing_devices = [
                    torch.device(passing_devices) for _ in range(self.num_workers)
                ]
            elif isinstance(passing_devices, Sequence):
                if len(passing_devices) != self.num_workers:
                    raise RuntimeError(
                        device_err_msg("passing_devices", passing_devices)
                    )
                self.passing_devices = [
                    torch.device(_passing_device) for _passing_device in passing_devices
                ]
            else:
                raise ValueError(
                    "passing_devices should be either a torch.device or equivalent or an iterable of devices. "
                    f"Found {type(passing_devices)} instead."
                )

        self.total_frames = total_frames if total_frames > 0 else float("inf")
        self.reset_at_each_iter = reset_at_each_iter
        self.postprocs = postproc
        self.max_frames_per_traj = max_frames_per_traj
        self.frames_per_batch = frames_per_batch
        self.seed = seed
        self.reset_when_done = reset_when_done
        if split_trajs is None:
            if not self.reset_when_done:
                split_trajs = False
            else:
                split_trajs = True
        elif not self.reset_when_done and split_trajs:
            raise RuntimeError(
                "Cannot split trajectories when reset_when_done is False."
            )
        self.split_trajs = split_trajs
        self.pin_memory = pin_memory
        self.init_random_frames = init_random_frames
        self.update_at_each_batch = update_at_each_batch
        self.init_with_lag = init_with_lag
        self.exploration_mode = exploration_mode
        self.frames_per_worker = np.inf
        self._run_processes()
        self._exclude_private_keys = True

    @property
    def frames_per_batch_worker(self):
        raise NotImplementedError

    def update_policy_weights_(self) -> None:
        for _device in self._policy_dict:
            if self._get_weights_fn_dict[_device] is not None:
                self._policy_dict[_device].load_state_dict(
                    self._get_weights_fn_dict[_device]()
                )

    @property
    def _queue_len(self) -> int:
        raise NotImplementedError

    def _run_processes(self) -> None:
        queue_out = mp.Queue(self._queue_len)  # sends data from proc to main
        self.procs = []
        self.pipes = []
        for i, (env_fun, env_fun_kwargs) in enumerate(
            zip(self.create_env_fn, self.create_env_kwargs)
        ):
            _device = self.devices[i]
            _passing_device = self.passing_devices[i]
            pipe_parent, pipe_child = mp.Pipe()  # send messages to procs
            if env_fun.__class__.__name__ != "EnvCreator" and not isinstance(
                env_fun, EnvBase
            ):  # to avoid circular imports
                env_fun = CloudpickleWrapper(env_fun)

            kwargs = {
                "pipe_parent": pipe_parent,
                "pipe_child": pipe_child,
                "queue_out": queue_out,
                "create_env_fn": env_fun,
                "create_env_kwargs": env_fun_kwargs,
                "policy": self._policy_dict[_device],
                "frames_per_worker": self.frames_per_worker,
                "max_frames_per_traj": self.max_frames_per_traj,
                "frames_per_batch": self.frames_per_batch_worker,
                "reset_at_each_iter": self.reset_at_each_iter,
                "device": _device,
                "passing_device": _passing_device,
                "seed": self.seed,
                "pin_memory": self.pin_memory,
                "init_with_lag": self.init_with_lag,
                "exploration_mode": self.exploration_mode,
                "reset_when_done": self.reset_when_done,
                "idx": i,
            }
            proc = mp.Process(target=_main_async_collector, kwargs=kwargs)
            # proc.daemon can't be set as daemonic processes may be launched by the process itself
            proc.start()
            pipe_child.close()
            self.procs.append(proc)
            self.pipes.append(pipe_parent)
            msg = pipe_parent.recv()
            if msg != "instantiated":
                raise RuntimeError(msg)
        self.queue_out = queue_out
        self.closed = False

    def __del__(self):
        self.shutdown()

    def shutdown(self) -> None:
        """Shuts down all processes. This operation is irreversible."""
        self._shutdown_main()

    def _shutdown_main(self) -> None:
        if self.closed:
            return
        _check_for_faulty_process(self.procs)
        self.closed = True
        for idx in range(self.num_workers):
            self.pipes[idx].send((None, "close"))

        for idx in range(self.num_workers):
            msg = self.pipes[idx].recv()
            if msg != "closed":
                raise RuntimeError(f"got {msg} but expected 'close'")

        for proc in self.procs:
            proc.join()

        self.queue_out.close()
        for pipe in self.pipes:
            pipe.close()

    def set_seed(self, seed: int, static_seed: bool = False) -> int:
        """Sets the seeds of the environments stored in the DataCollector.

        Args:
            seed: integer representing the seed to be used for the environment.
            static_seed (bool, optional): if True, the seed is not incremented.
                Defaults to False

        Returns:
            Output seed. This is useful when more than one environment is
            contained in the DataCollector, as the seed will be incremented for
            each of these. The resulting seed is the seed of the last
            environment.

        Examples:
            >>> env_fn = lambda: GymEnv("Pendulum-v0")
            >>> env_fn_parallel = lambda: ParallelEnv(6, env_fn)
            >>> collector = SyncDataCollector(env_fn_parallel)
            >>> out_seed = collector.set_seed(1)  # out_seed = 6

        """
        _check_for_faulty_process(self.procs)
        for idx in range(self.num_workers):
            self.pipes[idx].send(((seed, static_seed), "seed"))
            new_seed, msg = self.pipes[idx].recv()
            if msg != "seeded":
                raise RuntimeError(f"Expected msg='seeded', got {msg}")
            seed = new_seed
        self.reset()
        return seed

    def reset(self, reset_idx: Optional[Sequence[bool]] = None) -> None:
        """Resets the environments to a new initial state.

        Args:
            reset_idx: Optional. Sequence indicating which environments have
                to be reset. If None, all environments are reset.

        """
        _check_for_faulty_process(self.procs)

        if reset_idx is None:
            reset_idx = [True for _ in range(self.num_workers)]
        for idx in range(self.num_workers):
            if reset_idx[idx]:
                self.pipes[idx].send((None, "reset"))
        for idx in range(self.num_workers):
            if reset_idx[idx]:
                j, msg = self.pipes[idx].recv()
                if msg != "reset":
                    raise RuntimeError(f"Expected msg='reset', got {msg}")

    def state_dict(self) -> OrderedDict:
        """Returns the state_dict of the data collector.

        Each field represents a worker containing its own state_dict.

        """
        for idx in range(self.num_workers):
            self.pipes[idx].send((None, "state_dict"))
        state_dict = OrderedDict()
        for idx in range(self.num_workers):
            _state_dict, msg = self.pipes[idx].recv()
            if msg != "state_dict":
                raise RuntimeError(f"Expected msg='state_dict', got {msg}")
            state_dict[f"worker{idx}"] = _state_dict

        return state_dict

    def load_state_dict(self, state_dict: OrderedDict) -> None:
        """Loads the state_dict on the workers.

        Args:
            state_dict (OrderedDict): state_dict of the form
                ``{"worker0": state_dict0, "worker1": state_dict1}``.

        """
        for idx in range(self.num_workers):
            self.pipes[idx].send((state_dict[f"worker{idx}"], "load_state_dict"))
        for idx in range(self.num_workers):
            _, msg = self.pipes[idx].recv()
            if msg != "loaded":
                raise RuntimeError(f"Expected msg='loaded', got {msg}")


class MultiSyncDataCollector(_MultiDataCollector):
    """Runs a given number of DataCollectors on separate processes synchronously.

    The collection starts when the next item of the collector is queried,
    and no environment step is computed in between the reception of a batch of
    trajectory and the start of the next collection.
    This class can be safely used with online RL algorithms.

    """

    __doc__ += _MultiDataCollector.__doc__

    @property
    def frames_per_batch_worker(self):
        return -(-self.frames_per_batch // self.num_workers)

    @property
    def _queue_len(self) -> int:
        return self.num_workers

    def iterator(self) -> Iterator[TensorDictBase]:
        i = -1
        frames = 0
        out_tensordicts_shared = OrderedDict()
        dones = [False for _ in range(self.num_workers)]
        workers_frames = [0 for _ in range(self.num_workers)]
        same_device = None
        while not all(dones) and frames < self.total_frames:
            _check_for_faulty_process(self.procs)
            if self.update_at_each_batch:
                self.update_policy_weights_()

            for idx in range(self.num_workers):
                if frames < self.init_random_frames:
                    msg = "continue_random"
                else:
                    msg = "continue"
                self.pipes[idx].send((None, msg))

            i += 1
            max_traj_idx = None
            for _ in range(self.num_workers):
                new_data, j = self.queue_out.get()
                if j == 0:
                    data, idx = new_data
                    out_tensordicts_shared[idx] = data
                else:
                    idx = new_data
                workers_frames[idx] = (
                    workers_frames[idx] + out_tensordicts_shared[idx].numel()
                )

                if workers_frames[idx] >= self.total_frames:
                    print(f"{idx} is done!")
                    dones[idx] = True
            # we have to correct the traj_ids to make sure that they don't overlap
            for idx in range(self.num_workers):
                traj_ids = out_tensordicts_shared[idx].get("traj_ids")
                if max_traj_idx is not None:
                    traj_ids += max_traj_idx
                    # out_tensordicts_shared[idx].set("traj_ids", traj_ids)
                max_traj_idx = traj_ids.max().item() + 1
                # out = out_tensordicts_shared[idx]
            if same_device is None:
                prev_device = None
                same_device = True
                for item in out_tensordicts_shared.values():
                    if prev_device is None:
                        prev_device = item.device
                    else:
                        same_device = same_device and (item.device == prev_device)
            if same_device:
                out = torch.cat(list(out_tensordicts_shared.values()), 0)
            else:
                out = torch.cat(
                    [item.cpu() for item in out_tensordicts_shared.values()], 0
                )

            if self.split_trajs:
                out = split_trajectories(out)
                frames += out.get("mask").sum()
            else:
                frames += prod(out.shape)
            if self.postprocs:
                self.postprocs = self.postprocs.to(out.device)
                out = self.postprocs(out)
            if self._exclude_private_keys:
                excluded_keys = [key for key in out.keys() if key.startswith("_")]
                out = out.exclude(*excluded_keys)
            yield out

        del out_tensordicts_shared
        # We shall not call shutdown just yet as user may want to retrieve state_dict
        # self._shutdown_main()


class MultiaSyncDataCollector(_MultiDataCollector):
    """Runs a given number of DataCollectors on separate processes asynchronously.

    The collection keeps on occuring on all processes even between the time
    the batch of rollouts is collected and the next call to the iterator.
    This class can be safely used with offline RL algorithms.

    """

    __doc__ += _MultiDataCollector.__doc__

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.out_tensordicts = {}
        self.running = False

        if self.postprocs is not None:
            postproc = self.postprocs
            self.postprocs = {}
            for _device in self.passing_devices:
                if _device not in self.postprocs:
                    self.postprocs[_device] = deepcopy(postproc).to(_device)

    @property
    def frames_per_batch_worker(self):
        return self.frames_per_batch

    def _get_from_queue(self, timeout=None) -> Tuple[int, int, TensorDictBase]:
        new_data, j = self.queue_out.get(timeout=timeout)
        if j == 0:
            data, idx = new_data
            self.out_tensordicts[idx] = data
        else:
            idx = new_data
        # we clone the data to make sure that we'll be working with a fixed copy
        out = self.out_tensordicts[idx].clone()
        return idx, j, out

    @property
    def _queue_len(self) -> int:
        return 1

    def iterator(self) -> Iterator[TensorDictBase]:
        if self.update_at_each_batch:
            self.update_policy_weights_()

        for i in range(self.num_workers):
            if self.init_random_frames > 0:
                self.pipes[i].send((None, "continue_random"))
            else:
                self.pipes[i].send((None, "continue"))
        self.running = True
        i = -1
        self._frames = 0

        dones = [False for _ in range(self.num_workers)]
        workers_frames = [0 for _ in range(self.num_workers)]
        while self._frames < self.total_frames:
            _check_for_faulty_process(self.procs)
            i += 1
            idx, j, out = self._get_from_queue()

            worker_frames = out.numel()
            if self.split_trajs:
                out = split_trajectories(out)
            self._frames += worker_frames
            workers_frames[idx] = workers_frames[idx] + worker_frames
            if self.postprocs:
                out = self.postprocs[out.device](out)

            # the function blocks here until the next item is asked, hence we send the message to the
            # worker to keep on working in the meantime before the yield statement
            if workers_frames[idx] < self.frames_per_worker:
                if self._frames < self.init_random_frames:
                    msg = "continue_random"
                else:
                    msg = "continue"
                self.pipes[idx].send((idx, msg))
            else:
                print(f"{idx} is done!")
                dones[idx] = True
            if self._exclude_private_keys:
                excluded_keys = [key for key in out.keys() if key.startswith("_")]
                out = out.exclude(*excluded_keys)
            yield out

        # We don't want to shutdown yet, the user may want to call state_dict before
        # self._shutdown_main()
        self.running = False

    def _shutdown_main(self) -> None:
        if hasattr(self, "out_tensordicts"):
            del self.out_tensordicts
        return super()._shutdown_main()

    def reset(self, reset_idx: Optional[Sequence[bool]] = None) -> None:
        super().reset(reset_idx)
        if self.queue_out.full():
            print("waiting")
            time.sleep(_TIMEOUT)  # wait until queue is empty
        if self.queue_out.full():
            raise Exception("self.queue_out is full")
        if self.running:
            for idx in range(self.num_workers):
                if self._frames < self.init_random_frames:
                    self.pipes[idx].send((idx, "continue_random"))
                else:
                    self.pipes[idx].send((idx, "continue"))


class aSyncDataCollector(MultiaSyncDataCollector):
    """Runs a single DataCollector on a separate process.

    This is mostly useful for offline RL paradigms where the policy being
    trained can differ from the policy used to collect data. In online
    settings, a regular DataCollector should be preferred. This class is
    merely a wrapper around a MultiaSyncDataCollector where a single process
    is being created.

    Args:
        create_env_fn (Callabled): Callable returning an instance of EnvBase
        policy (Callable, optional): Instance of ProbabilisticTensorDictModule class.
            Must accept TensorDictBase object as input.
        total_frames (int): lower bound of the total number of frames returned
            by the collector. In parallel settings, the actual number of
            frames may well be greater than this as the closing signals are
            sent to the workers only once the total number of frames has
            been collected on the server.
        create_env_kwargs (dict, optional): A dictionary with the arguments
            used to create an environment
        max_frames_per_traj: Maximum steps per trajectory. Note that a
            trajectory can span over multiple batches (unless
            reset_at_each_iter is set to True, see below). Once a trajectory
            reaches n_steps_max, the environment is reset. If the
            environment wraps multiple environments together, the number of
            steps is tracked for each environment independently. Negative
            values are allowed, in which case this argument is ignored.
            Default is -1 (i.e. no maximum number of steps)
        frames_per_batch (int): Time-length of a batch.
            reset_at_each_iter and frames_per_batch == n_steps_max are equivalent configurations.
            default: 200
        init_random_frames (int): Number of frames for which the policy is ignored before it is called.
            This feature is mainly intended to be used in offline/model-based settings, where a batch of random
            trajectories can be used to initialize training.
            default=-1 (i.e. no random frames)
        reset_at_each_iter (bool): Whether or not environments should be reset for each batch.
            default=False.
        postproc (callable, optional): A PostProcessor is an object that will read a batch of data and process it in a
            useful format for training.
            default: None.
        split_trajs (bool): Boolean indicating whether the resulting TensorDict should be split according to the trajectories.
            See utils.split_trajectories for more information.
        device (int, str, torch.device, optional): The device on which the
            policy will be placed. If it differs from the input policy
            device, the update_policy_weights_() method should be queried
            at appropriate times during the training loop to accommodate for
            the lag between parameter configuration at various times.
            Default is `None` (i.e. policy is kept on its original device)
        passing_device (int, str, torch.device, optional): The device on which
            the output TensorDict will be stored. For long trajectories,
            it may be necessary to store the data on a different.
            device than the one where the policy is stored. Default is None.
        update_at_each_batch (bool): if True, the policy weights will be updated every time a batch of trajectories
            is collected.
            default=False
        init_with_lag (bool, optional): if True, the first trajectory will be truncated earlier at a random step.
            This is helpful to desynchronize the environments, such that steps do no match in all collected rollouts.
            default = True

    """

    def __init__(
        self,
        create_env_fn: Callable[[], EnvBase],
        policy: Optional[
            Union[
                ProbabilisticTensorDictModule,
                Callable[[TensorDictBase], TensorDictBase],
            ]
        ] = None,
        total_frames: Optional[int] = -1,
        create_env_kwargs: Optional[dict] = None,
        max_frames_per_traj: int = -1,
        frames_per_batch: int = 200,
        init_random_frames: int = -1,
        reset_at_each_iter: bool = False,
        postproc: Optional[Callable[[TensorDictBase], TensorDictBase]] = None,
        split_trajs: Optional[bool] = None,
        device: Optional[Union[int, str, torch.device]] = None,
        passing_device: Optional[Union[int, str, torch.device]] = None,
        seed: Optional[int] = None,
        pin_memory: bool = False,
        **kwargs,
    ):
        super().__init__(
            create_env_fn=[create_env_fn],
            policy=policy,
            total_frames=total_frames,
            create_env_kwargs=[create_env_kwargs],
            max_frames_per_traj=max_frames_per_traj,
            frames_per_batch=frames_per_batch,
            reset_at_each_iter=reset_at_each_iter,
            init_random_frames=init_random_frames,
            postproc=postproc,
            split_trajs=split_trajs,
            devices=[device] if device is not None else None,
            passing_devices=[passing_device] if passing_device is not None else None,
            seed=seed,
            pin_memory=pin_memory,
            **kwargs,
        )


def _main_async_collector(
    pipe_parent: connection.Connection,
    pipe_child: connection.Connection,
    queue_out: queues.Queue,
    create_env_fn: Union[EnvBase, "EnvCreator", Callable[[], EnvBase]],  # noqa: F821
    create_env_kwargs: Dict[str, Any],
    policy: Callable[[TensorDictBase], TensorDictBase],
    frames_per_worker: int,
    max_frames_per_traj: int,
    frames_per_batch: int,
    reset_at_each_iter: bool,
    device: Optional[Union[torch.device, str, int]],
    passing_device: Optional[Union[torch.device, str, int]],
    seed: Union[int, Sequence],
    pin_memory: bool,
    idx: int = 0,
    init_with_lag: bool = False,
    exploration_mode: str = DEFAULT_EXPLORATION_MODE,
    reset_when_done: bool = True,
    verbose: bool = False,
) -> None:
    pipe_parent.close()
    #  init variables that will be cleared when closing
    tensordict = data = d = data_in = dc = dc_iter = None

    dc = SyncDataCollector(
        create_env_fn,
        create_env_kwargs=create_env_kwargs,
        policy=policy,
        total_frames=-1,
        max_frames_per_traj=max_frames_per_traj,
        frames_per_batch=frames_per_batch,
        reset_at_each_iter=reset_at_each_iter,
        postproc=None,
        split_trajs=False,
        device=device,
        seed=seed,
        pin_memory=pin_memory,
        passing_device=passing_device,
        return_in_place=True,
        init_with_lag=init_with_lag,
        exploration_mode=exploration_mode,
        reset_when_done=reset_when_done,
        return_same_td=True,
    )
    if verbose:
        print("Sync data collector created")
    dc_iter = iter(dc)
    j = 0
    pipe_child.send("instantiated")

    has_timed_out = False
    counter = 0
    while True:
        _timeout = _TIMEOUT if not has_timed_out else 1e-3
        if pipe_child.poll(_timeout):
            counter = 0
            data_in, msg = pipe_child.recv()
            if verbose:
                print(f"worker {idx} received {msg}")
        else:
            if verbose:
                print(f"poll failed, j={j}")
            # default is "continue" (after first iteration)
            # this is expected to happen if queue_out reached the timeout, but no new msg was waiting in the pipe
            # in that case, the main process probably expects the worker to continue collect data
            if has_timed_out:
                counter = 0
                # has_timed_out is True if the process failed to send data, which will
                # typically occur if main has taken another batch (i.e. the queue is Full).
                # In this case, msg is the previous msg sent by main, which will typically be "continue"
                # If it's not the case, it is not expected that has_timed_out is True.
                if msg not in ("continue", "continue_random"):
                    raise RuntimeError(f"Unexpected message after time out: msg={msg}")
            else:
                # if has_timed_out is False, then the time out does not come from the fact that the queue is Full.
                # this means that our process has been waiting for a command from main in vain, while main was not
                # receiving data.
                # This will occur if main is busy doing something else (e.g. computing loss etc).
                counter += _timeout
                if counter >= (_MAX_IDLE_COUNT * _TIMEOUT):
                    raise RuntimeError(
                        f"This process waited for {counter} seconds "
                        f"without receiving a command from main. Consider increasing the maximum idle count "
                        f"if this is expected via the environment variable MAX_IDLE_COUNT "
                        f"(current value is {_MAX_IDLE_COUNT})."
                        f"\nIf this occurs at the end of a function, it means that your collector has not been "
                        f"collected, consider calling `collector.shutdown()` or `del collector` at the end of the function."
                    )
                continue
        if msg in ("continue", "continue_random"):
            if msg == "continue_random":
                dc.init_random_frames = float("inf")
            else:
                dc.init_random_frames = -1

            d = next(dc_iter)
            if pipe_child.poll(_MIN_TIMEOUT):
                # in this case, main send a message to the worker while it was busy collecting trajectories.
                # In that case, we skip the collected trajectory and get the message from main. This is faster than
                # sending the trajectory in the queue until timeout when it's never going to be received.
                continue
            if j == 0:
                tensordict = d
                if passing_device is not None and tensordict.device != passing_device:
                    raise RuntimeError(
                        f"expected device to be {passing_device} but got {tensordict.device}"
                    )
                tensordict.share_memory_()
                data = (tensordict, idx)
            else:
                if d is not tensordict:
                    raise RuntimeError(
                        "SyncDataCollector should return the same tensordict modified in-place."
                    )
                data = idx  # flag the worker that has sent its data
            try:
                queue_out.put((data, j), timeout=_TIMEOUT)
                if verbose:
                    print(f"worker {idx} successfully sent data")
                j += 1
                has_timed_out = False
                continue
            except queue.Full:
                if verbose:
                    print(f"worker {idx} has timed out")
                has_timed_out = True
                continue
            # pipe_child.send("done")

        elif msg == "update":
            dc.update_policy_weights_()
            pipe_child.send((j, "updated"))
            has_timed_out = False
            continue

        elif msg == "seed":
            data_in, static_seed = data_in
            new_seed = dc.set_seed(data_in, static_seed=static_seed)
            torch.manual_seed(data_in)
            np.random.seed(data_in)
            pipe_child.send((new_seed, "seeded"))
            has_timed_out = False
            continue

        elif msg == "reset":
            dc.reset()
            pipe_child.send((j, "reset"))
            continue

        elif msg == "state_dict":
            state_dict = dc.state_dict()
            # send state_dict to cpu first
            state_dict = recursive_map_to_cpu(state_dict)
            pipe_child.send((state_dict, "state_dict"))
            has_timed_out = False
            continue

        elif msg == "load_state_dict":
            state_dict = data_in
            dc.load_state_dict(state_dict)
            pipe_child.send((j, "loaded"))
            has_timed_out = False
            continue

        elif msg == "close":
            del tensordict, data, d, data_in
            dc.shutdown()
            del dc, dc_iter
            pipe_child.send("closed")
            if verbose:
                print(f"collector {idx} closed")
            break

        else:
            raise Exception(f"Unrecognized message {msg}")