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Source code for torchrl.envs.common

# 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.

from __future__ import annotations

import abc
import functools
import warnings
from copy import deepcopy
from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple

import numpy as np
import torch
import torch.nn as nn
from tensordict import LazyStackedTensorDict, TensorDictBase, unravel_key
from tensordict.base import NO_DEFAULT
from tensordict.utils import NestedKey
from torchrl._utils import (
    _ends_with,
    _replace_last,
    implement_for,
    prod,
    seed_generator,
)

from torchrl.data.tensor_specs import (
    CompositeSpec,
    DiscreteTensorSpec,
    TensorSpec,
    UnboundedContinuousTensorSpec,
)
from torchrl.data.utils import DEVICE_TYPING
from torchrl.envs.utils import (
    _make_compatible_policy,
    _repr_by_depth,
    _StepMDP,
    _terminated_or_truncated,
    _update_during_reset,
    get_available_libraries,
)

LIBRARIES = get_available_libraries()


def _tensor_to_np(t):
    return t.detach().cpu().numpy()


dtype_map = {
    torch.float: np.float32,
    torch.double: np.float64,
    torch.bool: bool,
}


[docs]class EnvMetaData: """A class for environment meta-data storage and passing in multiprocessed settings.""" def __init__( self, *, tensordict: TensorDictBase, specs: CompositeSpec, batch_size: torch.Size, env_str: str, device: torch.device, batch_locked: bool, device_map: dict, ): self.device = device self.tensordict = tensordict self.specs = specs self.batch_size = batch_size self.env_str = env_str self.batch_locked = batch_locked self.device_map = device_map @property def tensordict(self): return self._tensordict.to(self.device) @property def specs(self): return self._specs.to(self.device) @tensordict.setter def tensordict(self, value: TensorDictBase): self._tensordict = value.to("cpu") @specs.setter def specs(self, value: CompositeSpec): self._specs = value.to("cpu") @staticmethod def metadata_from_env(env) -> EnvMetaData: tensordict = env.fake_tensordict().clone() for done_key in env.done_keys: tensordict.set( _replace_last(done_key, "_reset"), torch.zeros_like(tensordict.get(("next", done_key))), ) specs = env.specs.to("cpu") batch_size = env.batch_size env_str = str(env) device = env.device specs = specs.to("cpu") batch_locked = env.batch_locked # we need to save the device map, as the tensordict will be placed on cpu device_map = {} def fill_device_map(name, val, device_map=device_map): device_map[name] = val.device tensordict.named_apply(fill_device_map, nested_keys=True, filter_empty=True) return EnvMetaData( tensordict=tensordict, specs=specs, batch_size=batch_size, env_str=env_str, device=device, batch_locked=batch_locked, device_map=device_map, ) def expand(self, *size: int) -> EnvMetaData: tensordict = self.tensordict.expand(*size).clone() batch_size = torch.Size(list(size)) return EnvMetaData( tensordict=tensordict, specs=self.specs.expand(*size), batch_size=batch_size, env_str=self.env_str, device=self.device, batch_locked=self.batch_locked, device_map=self.device_map, ) def clone(self): return EnvMetaData( tensordict=self.tensordict.clone(), specs=self.specs.clone(), batch_size=torch.Size([*self.batch_size]), env_str=deepcopy(self.env_str), device=self.device, batch_locked=self.batch_locked, device_map=self.device_map, ) def to(self, device: DEVICE_TYPING) -> EnvMetaData: if device is not None: device = torch.device(device) device_map = {key: device for key in self.device_map} tensordict = self.tensordict.contiguous().to(device) specs = self.specs.to(device) return EnvMetaData( tensordict=tensordict, specs=specs, batch_size=self.batch_size, env_str=self.env_str, device=device, batch_locked=self.batch_locked, device_map=device_map, )
class _EnvPostInit(abc.ABCMeta): def __call__(cls, *args, **kwargs): auto_reset = kwargs.pop("auto_reset", False) auto_reset_replace = kwargs.pop("auto_reset_replace", True) instance: EnvBase = super().__call__(*args, **kwargs) # we create the done spec by adding a done/terminated entry if one is missing instance._create_done_specs() # we access lazy attributed to make sure they're built properly. # This isn't done in `__init__` because we don't know if supre().__init__ # will be called before or after the specs, batch size etc are set. _ = instance.done_spec _ = instance.reward_spec _ = instance.state_spec if auto_reset: from torchrl.envs.transforms.transforms import ( AutoResetEnv, AutoResetTransform, ) return AutoResetEnv( instance, AutoResetTransform(replace=auto_reset_replace) ) return instance
[docs]class EnvBase(nn.Module, metaclass=_EnvPostInit): """Abstract environment parent class. Keyword Args: device (torch.device): The device of the environment. Deviceless environments are allowed (device=None). If not ``None``, all specs will be cast on that device and it is expected that all inputs and outputs will live on that device. Defaults to ``None``. batch_size (torch.Size or equivalent, optional): batch-size of the environment. Corresponds to the leading dimension of all the input and output tensordicts the environment reads and writes. Defaults to an empty batch-size. run_type_checks (bool, optional): If ``True``, type-checks will occur at every reset and every step. Defaults to ``False``. allow_done_after_reset (bool, optional): if ``True``, an environment can be done after a call to :meth:`~.reset` is made. Defaults to ``False``. Attributes: done_spec (CompositeSpec): equivalent to ``full_done_spec`` as all ``done_specs`` contain at least a ``"done"`` and a ``"terminated"`` entry action_spec (TensorSpec): the spec of the action. Links to the spec of the leaf action if only one action tensor is to be expected. Otherwise links to ``full_action_spec``. observation_spec (CompositeSpec): equivalent to ``full_observation_spec``. reward_spec (TensorSpec): the spec of the reward. Links to the spec of the leaf reward if only one reward tensor is to be expected. Otherwise links to ``full_reward_spec``. state_spec (CompositeSpec): equivalent to ``full_state_spec``. full_done_spec (CompositeSpec): a composite spec such that ``full_done_spec.zero()`` returns a tensordict containing only the leaves encoding the done status of the environment. full_action_spec (CompositeSpec): a composite spec such that ``full_action_spec.zero()`` returns a tensordict containing only the leaves encoding the action of the environment. full_observation_spec (CompositeSpec): a composite spec such that ``full_observation_spec.zero()`` returns a tensordict containing only the leaves encoding the observation of the environment. full_reward_spec (CompositeSpec): a composite spec such that ``full_reward_spec.zero()`` returns a tensordict containing only the leaves encoding the reward of the environment. full_state_spec (CompositeSpec): a composite spec such that ``full_state_spec.zero()`` returns a tensordict containing only the leaves encoding the inputs (actions excluded) of the environment. batch_size (torch.Size): The batch-size of the environment. device (torch.device): the device where the input/outputs of the environment are to be expected. Can be ``None``. Methods: step (TensorDictBase -> TensorDictBase): step in the environment reset (TensorDictBase, optional -> TensorDictBase): reset the environment set_seed (int -> int): sets the seed of the environment rand_step (TensorDictBase, optional -> TensorDictBase): random step given the action spec rollout (Callable, ... -> TensorDictBase): executes a rollout in the environment with the given policy (or random steps if no policy is provided) Examples: >>> from torchrl.envs import EnvBase >>> class CounterEnv(EnvBase): ... def __init__(self, batch_size=(), device=None, **kwargs): ... self.observation_spec = CompositeSpec( ... count=UnboundedContinuousTensorSpec(batch_size, device=device, dtype=torch.int64)) ... self.action_spec = UnboundedContinuousTensorSpec(batch_size, device=device, dtype=torch.int8) ... # done spec and reward spec are set automatically ... def _step(self, tensordict): ... >>> from torchrl.envs.libs.gym import GymEnv >>> env = GymEnv("Pendulum-v1") >>> env.batch_size # how many envs are run at once torch.Size([]) >>> env.input_spec CompositeSpec( full_state_spec: None, full_action_spec: CompositeSpec( action: BoundedTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) >>> env.action_spec BoundedTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous) >>> env.observation_spec CompositeSpec( observation: BoundedTensorSpec( shape=torch.Size([3]), space=ContinuousBox( low=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])) >>> env.reward_spec UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=None, device=cpu, dtype=torch.float32, domain=continuous) >>> env.done_spec DiscreteTensorSpec( shape=torch.Size([1]), space=DiscreteBox(n=2), device=cpu, dtype=torch.bool, domain=discrete) >>> # the output_spec contains all the expected outputs >>> env.output_spec CompositeSpec( full_reward_spec: CompositeSpec( reward: UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=None, device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])), full_observation_spec: CompositeSpec( observation: BoundedTensorSpec( shape=torch.Size([3]), space=ContinuousBox( low=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])), full_done_spec: CompositeSpec( done: DiscreteTensorSpec( shape=torch.Size([1]), space=DiscreteBox(n=2), device=cpu, dtype=torch.bool, domain=discrete), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) """ def __init__( self, *, device: DEVICE_TYPING = None, batch_size: Optional[torch.Size] = None, run_type_checks: bool = False, allow_done_after_reset: bool = False, ): self.__dict__.setdefault("_batch_size", None) if device is not None: self.__dict__["_device"] = torch.device(device) output_spec = self.__dict__.get("_output_spec", None) if output_spec is not None: self.__dict__["_output_spec"] = ( output_spec.to(self.device) if self.device is not None else output_spec ) input_spec = self.__dict__.get("_input_spec", None) if input_spec is not None: self.__dict__["_input_spec"] = ( input_spec.to(self.device) if self.device is not None else input_spec ) super().__init__() if "is_closed" not in self.__dir__(): self.is_closed = True if batch_size is not None: # we want an error to be raised if we pass batch_size but # it's already been set self.batch_size = torch.Size(batch_size) self._run_type_checks = run_type_checks self._allow_done_after_reset = allow_done_after_reset @classmethod def __new__(cls, *args, _inplace_update=False, _batch_locked=True, **kwargs): # inplace update will write tensors in-place on the provided tensordict. # This is risky, especially if gradients need to be passed (in-place copy # for tensors that are part of computational graphs will result in an error). # It can also lead to inconsistencies when calling rollout. cls._inplace_update = _inplace_update cls._batch_locked = _batch_locked cls._device = None # cached in_keys to be excluded from update when calling step cls._cache_in_keys = None # We may assign _input_spec to the cls, but it must be assigned to the instance # we pull it off, and place it back where it belongs _input_spec = None if hasattr(cls, "_input_spec"): _input_spec = cls._input_spec.clone() delattr(cls, "_input_spec") _output_spec = None if hasattr(cls, "_output_spec"): _output_spec = cls._output_spec.clone() delattr(cls, "_output_spec") env = super().__new__(cls) if _input_spec is not None: env.__dict__["_input_spec"] = _input_spec if _output_spec is not None: env.__dict__["_output_spec"] = _output_spec return env return super().__new__(cls) def __setattr__(self, key, value): if key in ( "_input_spec", "_observation_spec", "_action_spec", "_reward_spec", "_output_spec", "_state_spec", "_done_spec", ): raise AttributeError( "To set an environment spec, please use `env.observation_spec = obs_spec` (without the leading" " underscore)." ) return super().__setattr__(key, value) @property def batch_locked(self) -> bool: """Whether the environment can be used with a batch size different from the one it was initialized with or not. If True, the env needs to be used with a tensordict having the same batch size as the env. batch_locked is an immutable property. """ return self._batch_locked @batch_locked.setter def batch_locked(self, value: bool) -> None: raise RuntimeError("batch_locked is a read-only property") @property def run_type_checks(self) -> bool: return self._run_type_checks @run_type_checks.setter def run_type_checks(self, run_type_checks: bool) -> None: self._run_type_checks = run_type_checks @property def batch_size(self) -> torch.Size: """Number of envs batched in this environment instance organised in a `torch.Size()` object. Environment may be similar or different but it is assumed that they have little if not no interactions between them (e.g., multi-task or batched execution in parallel). """ _batch_size = self.__dict__["_batch_size"] if _batch_size is None: _batch_size = self._batch_size = torch.Size([]) return _batch_size @batch_size.setter def batch_size(self, value: torch.Size) -> None: self._batch_size = torch.Size(value) if ( hasattr(self, "output_spec") and self.output_spec.shape[: len(value)] != value ): self.output_spec.unlock_() self.output_spec.shape = value self.output_spec.lock_() if hasattr(self, "input_spec") and self.input_spec.shape[: len(value)] != value: self.input_spec.unlock_() self.input_spec.shape = value self.input_spec.lock_() @property def shape(self): """Equivalent to :attr:`~.batch_size`.""" return self.batch_size @property def device(self) -> torch.device: device = self.__dict__.get("_device", None) return device @device.setter def device(self, value: torch.device) -> None: device = self.__dict__.get("_device", None) if device is None: self.__dict__["_device"] = value return raise RuntimeError("device cannot be set. Call env.to(device) instead.") def ndimension(self): return len(self.batch_size) @property def ndim(self): return self.ndimension()
[docs] def append_transform( self, transform: "Transform" # noqa: F821 | Callable[[TensorDictBase], TensorDictBase], ) -> None: """Returns a transformed environment where the callable/transform passed is applied. Args: transform (Transform or Callable[[TensorDictBase], TensorDictBase]): the transform to apply to the environment. Examples: >>> from torchrl.envs import GymEnv >>> import torch >>> env = GymEnv("CartPole-v1") >>> loc = 0.5 >>> scale = 1.0 >>> transform = lambda data: data.set("observation", (data.get("observation") - loc)/scale) >>> env = env.append_transform(transform=transform) >>> print(env) TransformedEnv( env=GymEnv(env=CartPole-v1, batch_size=torch.Size([]), device=cpu), transform=_CallableTransform(keys=[])) """ from torchrl.envs.transforms.transforms import TransformedEnv return TransformedEnv(self, transform)
# Parent specs: input and output spec. @property def input_spec(self) -> TensorSpec: """Input spec. The composite spec containing all specs for data input to the environments. It contains: - "full_action_spec": the spec of the input actions - "full_state_spec": the spec of all other environment inputs This attibute is locked and should be read-only. Instead, to set the specs contained in it, use the respective properties. Examples: >>> from torchrl.envs.libs.gym import GymEnv >>> env = GymEnv("Pendulum-v1") >>> env.input_spec CompositeSpec( full_state_spec: None, full_action_spec: CompositeSpec( action: BoundedTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) """ input_spec = self.__dict__.get("_input_spec", None) if input_spec is None: input_spec = CompositeSpec( full_state_spec=None, shape=self.batch_size, device=self.device, ).lock_() self.__dict__["_input_spec"] = input_spec return input_spec @input_spec.setter def input_spec(self, value: TensorSpec) -> None: raise RuntimeError("input_spec is protected.") @property def output_spec(self) -> TensorSpec: """Output spec. The composite spec containing all specs for data output from the environments. It contains: - "full_reward_spec": the spec of reward - "full_done_spec": the spec of done - "full_observation_spec": the spec of all other environment outputs This attibute is locked and should be read-only. Instead, to set the specs contained in it, use the respective properties. Examples: >>> from torchrl.envs.libs.gym import GymEnv >>> env = GymEnv("Pendulum-v1") >>> env.output_spec CompositeSpec( full_reward_spec: CompositeSpec( reward: UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=None, device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])), full_observation_spec: CompositeSpec( observation: BoundedTensorSpec( shape=torch.Size([3]), space=ContinuousBox( low=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])), full_done_spec: CompositeSpec( done: DiscreteTensorSpec( shape=torch.Size([1]), space=DiscreteBox(n=2), device=cpu, dtype=torch.bool, domain=discrete), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) """ output_spec = self.__dict__.get("_output_spec", None) if output_spec is None: output_spec = CompositeSpec( shape=self.batch_size, device=self.device, ).lock_() self.__dict__["_output_spec"] = output_spec return output_spec @output_spec.setter def output_spec(self, value: TensorSpec) -> None: raise RuntimeError("output_spec is protected.") @property def action_keys(self) -> List[NestedKey]: """The action keys of an environment. By default, there will only be one key named "action". Keys are sorted by depth in the data tree. """ action_keys = self.__dict__.get("_action_keys", None) if action_keys is not None: return action_keys keys = self.input_spec["full_action_spec"].keys(True, True) if not len(keys): raise AttributeError("Could not find action spec") keys = sorted(keys, key=_repr_by_depth) self.__dict__["_action_keys"] = keys return keys @property def action_key(self) -> NestedKey: """The action key of an environment. By default, this will be "action". If there is more than one action key in the environment, this function will raise an exception. """ if len(self.action_keys) > 1: raise KeyError( "action_key requested but more than one key present in the environment" ) return self.action_keys[0] # Action spec: action specs belong to input_spec @property def action_spec(self) -> TensorSpec: """The ``action`` spec. The ``action_spec`` is always stored as a composite spec. If the action spec is provided as a simple spec, this will be returned. >>> env.action_spec = UnboundedContinuousTensorSpec(1) >>> env.action_spec UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous) If the action spec is provided as a composite spec and contains only one leaf, this function will return just the leaf. >>> env.action_spec = CompositeSpec({"nested": {"action": UnboundedContinuousTensorSpec(1)}}) >>> env.action_spec UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous) If the action spec is provided as a composite spec and has more than one leaf, this function will return the whole spec. >>> env.action_spec = CompositeSpec({"nested": {"action": UnboundedContinuousTensorSpec(1), "another_action": DiscreteTensorSpec(1)}}) >>> env.action_spec CompositeSpec( nested: CompositeSpec( action: UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), another_action: DiscreteTensorSpec( shape=torch.Size([]), space=DiscreteBox(n=1), device=cpu, dtype=torch.int64, domain=discrete), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) To retrieve the full spec passed, use: >>> env.input_spec["full_action_spec"] This property is mutable. Examples: >>> from torchrl.envs.libs.gym import GymEnv >>> env = GymEnv("Pendulum-v1") >>> env.action_spec BoundedTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous) """ try: action_spec = self.input_spec["full_action_spec"] except (KeyError, AttributeError): raise KeyError("Failed to find the action_spec.") if len(self.action_keys) > 1: out = action_spec else: try: out = action_spec[self.action_key] except KeyError: # the key may have changed raise KeyError( "The action_key attribute seems to have changed. " "This occurs when a action_spec is updated without " "calling `env.action_spec = new_spec`. " "Make sure you rely on this type of command " "to set the action and other specs." ) return out @action_spec.setter def action_spec(self, value: TensorSpec) -> None: try: self.input_spec.unlock_() device = self.input_spec._device try: delattr(self, "_action_keys") except AttributeError: pass if not hasattr(value, "shape"): raise TypeError( f"action_spec of type {type(value)} do not have a shape attribute." ) if value.shape[: len(self.batch_size)] != self.batch_size: raise ValueError( f"The value of spec.shape ({value.shape}) must match the env batch size ({self.batch_size})." ) if isinstance(value, CompositeSpec): for _ in value.values(True, True): # noqa: B007 break else: raise RuntimeError( "An empty CompositeSpec was passed for the action spec. " "This is currently not permitted." ) else: value = CompositeSpec( action=value.to(device), shape=self.batch_size, device=device ) self.input_spec["full_action_spec"] = value.to(device) finally: self.input_spec.lock_() @property def full_action_spec(self) -> CompositeSpec: """The full action spec. ``full_action_spec`` is a :class:`~torchrl.data.CompositeSpec`` instance that contains all the action entries. Examples: >>> from torchrl.envs import BraxEnv >>> for envname in BraxEnv.available_envs: ... break >>> env = BraxEnv(envname) >>> env.full_action_spec CompositeSpec( action: BoundedTensorSpec( shape=torch.Size([8]), space=ContinuousBox( low=Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])) """ return self.input_spec["full_action_spec"] @full_action_spec.setter def full_action_spec(self, spec: CompositeSpec) -> None: self.action_spec = spec # Reward spec @property def reward_keys(self) -> List[NestedKey]: """The reward keys of an environment. By default, there will only be one key named "reward". Keys are sorted by depth in the data tree. """ reward_keys = self.__dict__.get("_reward_keys", None) if reward_keys is not None: return reward_keys reward_keys = sorted(self.full_reward_spec.keys(True, True), key=_repr_by_depth) self.__dict__["_reward_keys"] = reward_keys return reward_keys @property def reward_key(self): """The reward key of an environment. By default, this will be "reward". If there is more than one reward key in the environment, this function will raise an exception. """ if len(self.reward_keys) > 1: raise KeyError( "reward_key requested but more than one key present in the environment" ) return self.reward_keys[0] # Reward spec: reward specs belong to output_spec @property def reward_spec(self) -> TensorSpec: """The ``reward`` spec. The ``reward_spec`` is always stored as a composite spec. If the reward spec is provided as a simple spec, this will be returned. >>> env.reward_spec = UnboundedContinuousTensorSpec(1) >>> env.reward_spec UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous) If the reward spec is provided as a composite spec and contains only one leaf, this function will return just the leaf. >>> env.reward_spec = CompositeSpec({"nested": {"reward": UnboundedContinuousTensorSpec(1)}}) >>> env.reward_spec UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous) If the reward spec is provided as a composite spec and has more than one leaf, this function will return the whole spec. >>> env.reward_spec = CompositeSpec({"nested": {"reward": UnboundedContinuousTensorSpec(1), "another_reward": DiscreteTensorSpec(1)}}) >>> env.reward_spec CompositeSpec( nested: CompositeSpec( reward: UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), another_reward: DiscreteTensorSpec( shape=torch.Size([]), space=DiscreteBox(n=1), device=cpu, dtype=torch.int64, domain=discrete), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) To retrieve the full spec passed, use: >>> env.output_spec["full_reward_spec"] This property is mutable. Examples: >>> from torchrl.envs.libs.gym import GymEnv >>> env = GymEnv("Pendulum-v1") >>> env.reward_spec UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=None, device=cpu, dtype=torch.float32, domain=continuous) """ try: reward_spec = self.output_spec["full_reward_spec"] except (KeyError, AttributeError): # populate the "reward" entry # this will be raised if there is not full_reward_spec (unlikely) or no reward_key # Since output_spec is lazily populated with an empty composite spec for # reward_spec, the second case is much more likely to occur. self.reward_spec = UnboundedContinuousTensorSpec( shape=(*self.batch_size, 1), device=self.device, ) reward_spec = self.output_spec["full_reward_spec"] reward_keys = self.reward_keys if len(reward_keys) > 1 or not len(reward_keys): return reward_spec else: return reward_spec[self.reward_keys[0]] @reward_spec.setter def reward_spec(self, value: TensorSpec) -> None: try: self.output_spec.unlock_() device = self.output_spec._device try: delattr(self, "_reward_keys") except AttributeError: pass if not hasattr(value, "shape"): raise TypeError( f"reward_spec of type {type(value)} do not have a shape " f"attribute." ) if value.shape[: len(self.batch_size)] != self.batch_size: raise ValueError( f"The value of spec.shape ({value.shape}) must match the env batch size ({self.batch_size})." ) if isinstance(value, CompositeSpec): for _ in value.values(True, True): # noqa: B007 break else: raise RuntimeError( "An empty CompositeSpec was passed for the reward spec. " "This is currently not permitted." ) else: value = CompositeSpec( reward=value.to(device), shape=self.batch_size, device=device ) for leaf in value.values(True, True): if len(leaf.shape) == 0: raise RuntimeError( "the reward_spec's leafs shape cannot be empty (this error" " usually comes from trying to set a reward_spec" " with a null number of dimensions. Try using a multidimensional" " spec instead, for instance with a singleton dimension at the tail)." ) self.output_spec["full_reward_spec"] = value.to(device) finally: self.output_spec.lock_() @property def full_reward_spec(self) -> CompositeSpec: """The full reward spec. ``full_reward_spec`` is a :class:`~torchrl.data.CompositeSpec`` instance that contains all the reward entries. Examples: >>> import gymnasium >>> from torchrl.envs import GymWrapper, TransformedEnv, RenameTransform >>> base_env = GymWrapper(gymnasium.make("Pendulum-v1")) >>> env = TransformedEnv(base_env, RenameTransform("reward", ("nested", "reward"))) >>> env.full_reward_spec CompositeSpec( nested: CompositeSpec( reward: UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), device=None, shape=torch.Size([])), device=cpu, shape=torch.Size([])) """ return self.output_spec["full_reward_spec"] @full_reward_spec.setter def full_reward_spec(self, spec: CompositeSpec) -> None: self.reward_spec = spec.to(self.device) if self.device is not None else spec # done spec @property def done_keys(self) -> List[NestedKey]: """The done keys of an environment. By default, there will only be one key named "done". Keys are sorted by depth in the data tree. """ done_keys = self.__dict__.get("_done_keys", None) if done_keys is not None: return done_keys done_keys = sorted(self.full_done_spec.keys(True, True), key=_repr_by_depth) self.__dict__["_done_keys"] = done_keys return done_keys @property def done_key(self): """The done key of an environment. By default, this will be "done". If there is more than one done key in the environment, this function will raise an exception. """ if len(self.done_keys) > 1: raise KeyError( "done_key requested but more than one key present in the environment" ) return self.done_keys[0] @property def full_done_spec(self) -> CompositeSpec: """The full done spec. ``full_done_spec`` is a :class:`~torchrl.data.CompositeSpec`` instance that contains all the done entries. It can be used to generate fake data with a structure that mimics the one obtained at runtime. Examples: >>> import gymnasium >>> from torchrl.envs import GymWrapper >>> env = GymWrapper(gymnasium.make("Pendulum-v1")) >>> env.full_done_spec CompositeSpec( done: DiscreteTensorSpec( shape=torch.Size([1]), space=DiscreteBox(n=2), device=cpu, dtype=torch.bool, domain=discrete), truncated: DiscreteTensorSpec( shape=torch.Size([1]), space=DiscreteBox(n=2), device=cpu, dtype=torch.bool, domain=discrete), device=cpu, shape=torch.Size([])) """ return self.output_spec["full_done_spec"] @full_done_spec.setter def full_done_spec(self, spec: CompositeSpec) -> None: self.done_spec = spec.to(self.device) if self.device is not None else spec # Done spec: done specs belong to output_spec @property def done_spec(self) -> TensorSpec: """The ``done`` spec. The ``done_spec`` is always stored as a composite spec. If the done spec is provided as a simple spec, this will be returned. >>> env.done_spec = DiscreteTensorSpec(2, dtype=torch.bool) >>> env.done_spec DiscreteTensorSpec( shape=torch.Size([]), space=DiscreteBox(n=2), device=cpu, dtype=torch.bool, domain=discrete) If the done spec is provided as a composite spec and contains only one leaf, this function will return just the leaf. >>> env.done_spec = CompositeSpec({"nested": {"done": DiscreteTensorSpec(2, dtype=torch.bool)}}) >>> env.done_spec DiscreteTensorSpec( shape=torch.Size([]), space=DiscreteBox(n=2), device=cpu, dtype=torch.bool, domain=discrete) If the done spec is provided as a composite spec and has more than one leaf, this function will return the whole spec. >>> env.done_spec = CompositeSpec({"nested": {"done": DiscreteTensorSpec(2, dtype=torch.bool), "another_done": DiscreteTensorSpec(2, dtype=torch.bool)}}) >>> env.done_spec CompositeSpec( nested: CompositeSpec( done: DiscreteTensorSpec( shape=torch.Size([]), space=DiscreteBox(n=2), device=cpu, dtype=torch.bool, domain=discrete), another_done: DiscreteTensorSpec( shape=torch.Size([]), space=DiscreteBox(n=2), device=cpu, dtype=torch.bool, domain=discrete), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) To always retrieve the full spec passed, use: >>> env.output_spec["full_done_spec"] This property is mutable. Examples: >>> from torchrl.envs.libs.gym import GymEnv >>> env = GymEnv("Pendulum-v1") >>> env.done_spec DiscreteTensorSpec( shape=torch.Size([1]), space=DiscreteBox(n=2), device=cpu, dtype=torch.bool, domain=discrete) """ done_spec = self.output_spec["full_done_spec"] return done_spec def _create_done_specs(self): """Reads through the done specs and makes it so that it's complete. If the done_specs contain only a ``"done"`` entry, a similar ``"terminated"`` entry is created. Same goes if only ``"terminated"`` key is present. If none of ``"done"`` and ``"terminated"`` can be found and the spec is not empty, nothing is changed. """ try: full_done_spec = self.output_spec["full_done_spec"] except KeyError: full_done_spec = CompositeSpec( shape=self.output_spec.shape, device=self.output_spec.device ) full_done_spec["done"] = DiscreteTensorSpec( n=2, shape=(*full_done_spec.shape, 1), dtype=torch.bool, device=self.device, ) full_done_spec["terminated"] = DiscreteTensorSpec( n=2, shape=(*full_done_spec.shape, 1), dtype=torch.bool, device=self.device, ) self.output_spec.unlock_() self.output_spec["full_done_spec"] = full_done_spec self.output_spec.lock_() return def check_local_done(spec): shape = None for key, item in list( spec.items() ): # list to avoid error due to in-loop changes # in the case where the spec is non-empty and there is no done and no terminated, we do nothing if key == "done" and "terminated" not in spec.keys(): spec["terminated"] = item.clone() elif key == "terminated" and "done" not in spec.keys(): spec["done"] = item.clone() elif isinstance(item, CompositeSpec): check_local_done(item) else: if shape is None: shape = item.shape continue # checks that all shape match if shape != item.shape: raise ValueError( f"All shapes should match in done_spec {spec} (shape={shape}, key={key})." ) # if the spec is empty, we need to add a done and terminated manually if spec.is_empty(): spec["done"] = DiscreteTensorSpec( n=2, shape=(*spec.shape, 1), dtype=torch.bool, device=self.device ) spec["terminated"] = DiscreteTensorSpec( n=2, shape=(*spec.shape, 1), dtype=torch.bool, device=self.device ) self.output_spec.unlock_() check_local_done(full_done_spec) self.output_spec["full_done_spec"] = full_done_spec self.output_spec.lock_() return @done_spec.setter def done_spec(self, value: TensorSpec) -> None: try: self.output_spec.unlock_() device = self.output_spec.device try: delattr(self, "_done_keys") except AttributeError: pass if not hasattr(value, "shape"): raise TypeError( f"done_spec of type {type(value)} do not have a shape " f"attribute." ) if value.shape[: len(self.batch_size)] != self.batch_size: raise ValueError( f"The value of spec.shape ({value.shape}) must match the env batch size ({self.batch_size})." ) if isinstance(value, CompositeSpec): for _ in value.values(True, True): # noqa: B007 break else: raise RuntimeError( "An empty CompositeSpec was passed for the done spec. " "This is currently not permitted." ) else: value = CompositeSpec( done=value.to(device), terminated=value.to(device), shape=self.batch_size, device=device, ) for leaf in value.values(True, True): if len(leaf.shape) == 0: raise RuntimeError( "the done_spec's leafs shape cannot be empty (this error" " usually comes from trying to set a reward_spec" " with a null number of dimensions. Try using a multidimensional" " spec instead, for instance with a singleton dimension at the tail)." ) self.output_spec["full_done_spec"] = value.to(device) self._create_done_specs() finally: self.output_spec.lock_() # observation spec: observation specs belong to output_spec @property def observation_spec(self) -> CompositeSpec: """Observation spec. Must be a :class:`torchrl.data.CompositeSpec` instance. The keys listed in the spec are directly accessible after reset and step. In TorchRL, even though they are not properly speaking "observations" all info, states, results of transforms etc. outputs from the environment are stored in the ``observation_spec``. Therefore, ``"observation_spec"`` should be thought as a generic data container for environment outputs that are not done or reward data. Examples: >>> from torchrl.envs.libs.gym import GymEnv >>> env = GymEnv("Pendulum-v1") >>> env.observation_spec CompositeSpec( observation: BoundedTensorSpec( shape=torch.Size([3]), space=ContinuousBox( low=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])) """ observation_spec = self.output_spec["full_observation_spec"] if observation_spec is None: observation_spec = CompositeSpec(shape=self.batch_size, device=self.device) self.output_spec.unlock_() self.output_spec["full_observation_spec"] = observation_spec self.output_spec.lock_() return observation_spec @observation_spec.setter def observation_spec(self, value: TensorSpec) -> None: try: self.output_spec.unlock_() if not isinstance(value, CompositeSpec): raise TypeError("The type of an observation_spec must be Composite.") elif value.shape[: len(self.batch_size)] != self.batch_size: raise ValueError( f"The value of spec.shape ({value.shape}) must match the env batch size ({self.batch_size})." ) if value.shape[: len(self.batch_size)] != self.batch_size: raise ValueError( f"The value of spec.shape ({value.shape}) must match the env batch size ({self.batch_size})." ) device = self.output_spec._device self.output_spec["full_observation_spec"] = ( value.to(device) if device is not None else value ) finally: self.output_spec.lock_() @property def full_observation_spec(self) -> CompositeSpec: return self.observation_spec @full_observation_spec.setter def full_observation_spec(self, spec: CompositeSpec): self.observation_spec = spec # state spec: state specs belong to input_spec @property def state_spec(self) -> CompositeSpec: """State spec. Must be a :class:`torchrl.data.CompositeSpec` instance. The keys listed here should be provided as input alongside actions to the environment. In TorchRL, even though they are not properly speaking "state" all inputs to the environment that are not actions are stored in the ``state_spec``. Therefore, ``"state_spec"`` should be thought as a generic data container for environment inputs that are not action data. Examples: >>> from torchrl.envs import BraxEnv >>> for envname in BraxEnv.available_envs: ... break >>> env = BraxEnv(envname) >>> env.state_spec CompositeSpec( state: CompositeSpec( pipeline_state: CompositeSpec( q: UnboundedContinuousTensorSpec( shape=torch.Size([15]), space=None, device=cpu, dtype=torch.float32, domain=continuous), [...], device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) """ state_spec = self.input_spec["full_state_spec"] if state_spec is None: state_spec = CompositeSpec(shape=self.batch_size, device=self.device) self.input_spec.unlock_() self.input_spec["full_state_spec"] = state_spec self.input_spec.lock_() return state_spec @state_spec.setter def state_spec(self, value: CompositeSpec) -> None: try: self.input_spec.unlock_() if value is None: self.input_spec["full_state_spec"] = CompositeSpec( device=self.device, shape=self.batch_size ) else: device = self.input_spec.device if not isinstance(value, CompositeSpec): raise TypeError("The type of an state_spec must be Composite.") elif value.shape[: len(self.batch_size)] != self.batch_size: raise ValueError( f"The value of spec.shape ({value.shape}) must match the env batch size ({self.batch_size})." ) if value.shape[: len(self.batch_size)] != self.batch_size: raise ValueError( f"The value of spec.shape ({value.shape}) must match the env batch size ({self.batch_size})." ) self.input_spec["full_state_spec"] = ( value.to(device) if device is not None else value ) finally: self.input_spec.lock_() @property def full_state_spec(self) -> CompositeSpec: """The full state spec. ``full_state_spec`` is a :class:`~torchrl.data.CompositeSpec`` instance that contains all the state entries (ie, the input data that is not action). Examples: >>> from torchrl.envs import BraxEnv >>> for envname in BraxEnv.available_envs: ... break >>> env = BraxEnv(envname) >>> env.full_state_spec CompositeSpec( state: CompositeSpec( pipeline_state: CompositeSpec( q: UnboundedContinuousTensorSpec( shape=torch.Size([15]), space=None, device=cpu, dtype=torch.float32, domain=continuous), [...], device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) """ return self.state_spec @full_state_spec.setter def full_state_spec(self, spec: CompositeSpec) -> None: self.state_spec = spec
[docs] def step(self, tensordict: TensorDictBase) -> TensorDictBase: """Makes a step in the environment. Step accepts a single argument, tensordict, which usually carries an 'action' key which indicates the action to be taken. Step will call an out-place private method, _step, which is the method to be re-written by EnvBase subclasses. Args: tensordict (TensorDictBase): Tensordict containing the action to be taken. If the input tensordict contains a ``"next"`` entry, the values contained in it will prevail over the newly computed values. This gives a mechanism to override the underlying computations. Returns: the input tensordict, modified in place with the resulting observations, done state and reward (+ others if needed). """ # sanity check self._assert_tensordict_shape(tensordict) next_preset = tensordict.get("next", None) next_tensordict = self._step(tensordict) next_tensordict = self._step_proc_data(next_tensordict) if next_preset is not None: # tensordict could already have a "next" key # this could be done more efficiently by not excluding but just passing # the necessary keys next_tensordict.update( next_preset.exclude(*next_tensordict.keys(True, True)) ) tensordict.set("next", next_tensordict) return tensordict
@classmethod def _complete_done( cls, done_spec: CompositeSpec, data: TensorDictBase ) -> TensorDictBase: """Completes the data structure at step time to put missing done keys.""" # by default, if a done key is missing, it is assumed that it is False # except in 2 cases: (1) there is a "done" but no "terminated" or (2) # there is a "terminated" but no "done". if done_spec.ndim: leading_dim = data.shape[: -done_spec.ndim] else: leading_dim = data.shape vals = {} i = -1 for i, (key, item) in enumerate(done_spec.items()): # noqa: B007 val = data.get(key, None) if isinstance(item, CompositeSpec): if val is not None: cls._complete_done(item, val) continue shape = (*leading_dim, *item.shape) if val is not None: if val.shape != shape: data.set(key, val.reshape(shape)) vals[key] = val if len(vals) < i + 1: # complete missing dones: we only want to do that if we don't have enough done values data_keys = set(data.keys()) done_spec_keys = set(done_spec.keys()) for key, item in done_spec.items(False, True): val = vals.get(key, None) if ( key == "done" and val is not None and "terminated" in done_spec_keys and "terminated" not in data_keys ): if "truncated" in data_keys: raise RuntimeError( "Cannot infer the value of terminated when only done and truncated are present." ) data.set("terminated", val) elif ( key == "terminated" and val is not None and "done" in done_spec_keys and "done" not in data_keys ): if "truncated" in data_keys: done = val | data.get("truncated") data.set("done", done) else: data.set("done", val) elif val is None: # we must keep this here: we only want to fill with 0s if we're sure # done should not be copied to terminated or terminated to done # in this case, just fill with 0s data.set(key, item.zero(leading_dim)) return data def _step_proc_data(self, next_tensordict_out): batch_size = self.batch_size dims = len(batch_size) leading_batch_size = ( next_tensordict_out.batch_size[:-dims] if dims else next_tensordict_out.shape ) for reward_key in self.reward_keys: reward = next_tensordict_out.get(reward_key) expected_reward_shape = torch.Size( [ *leading_batch_size, *self.output_spec["full_reward_spec"][reward_key].shape, ] ) actual_reward_shape = reward.shape if actual_reward_shape != expected_reward_shape: reward = reward.view(expected_reward_shape) next_tensordict_out.set(reward_key, reward) self._complete_done(self.full_done_spec, next_tensordict_out) if self.run_type_checks: for key, spec in self.observation_spec.items(): obs = next_tensordict_out.get(key) spec.type_check(obs) for reward_key in self.reward_keys: if ( next_tensordict_out.get(reward_key).dtype is not self.output_spec[ unravel_key(("full_reward_spec", reward_key)) ].dtype ): raise TypeError( f"expected reward.dtype to be {self.output_spec[unravel_key(('full_reward_spec',reward_key))]} " f"but got {next_tensordict_out.get(reward_key).dtype}" ) for done_key in self.done_keys: if ( next_tensordict_out.get(done_key).dtype is not self.output_spec["full_done_spec", done_key].dtype ): raise TypeError( f"expected done.dtype to be {self.output_spec['full_done_spec', done_key].dtype} but got {next_tensordict_out.get(done_key).dtype}" ) return next_tensordict_out def _get_in_keys_to_exclude(self, tensordict): if self._cache_in_keys is None: self._cache_in_keys = list( set(self.input_spec.keys(True)).intersection( tensordict.keys(True, True) ) ) return self._cache_in_keys
[docs] @classmethod def register_gym( cls, id: str, *, entry_point: Callable | None = None, transform: "Transform" | None = None, # noqa: F821 info_keys: List[NestedKey] | None = None, backend: str = None, to_numpy: bool = False, reward_threshold: float | None = None, nondeterministic: bool = False, max_episode_steps: int | None = None, order_enforce: bool = True, autoreset: bool = False, disable_env_checker: bool = False, apply_api_compatibility: bool = False, **kwargs, ): """Registers an environment in gym(nasium). This method is designed with the following scopes in mind: - Incorporate a TorchRL-first environment in a framework that uses Gym; - Incorporate another environment (eg, DeepMind Control, Brax, Jumanji, ...) in a framework that uses Gym. Args: id (str): the name of the environment. Should follow the `gym naming convention <https://www.gymlibrary.dev/content/environment_creation/#registering-envs>`_. Keyword Args: entry_point (callable, optional): the entry point to build the environment. If none is passed, the parent class will be used as entry point. Typically, this is used to register an environment that does not necessarily inherit from the base being used: >>> from torchrl.envs import DMControlEnv >>> DMControlEnv.register_gym("DMC-cheetah-v0", env_name="cheetah", task="run") >>> # equivalently >>> EnvBase.register_gym("DMC-cheetah-v0", entry_point=DMControlEnv, env_name="cheetah", task="run") transform (torchrl.envs.Transform): a transform (or list of transforms within a :class:`torchrl.envs.Compose` instance) to be used with the env. This arg can be passed during a call to :func:`~gym.make` (see example below). info_keys (List[NestedKey], optional): if provided, these keys will be used to build the info dictionary and will be excluded from the observation keys. This arg can be passed during a call to :func:`~gym.make` (see example below). .. warning:: It may be the case that using ``info_keys`` makes a spec empty because the content has been moved to the info dictionary. Gym does not like empty ``Dict`` in the specs, so this empty content should be removed with :class:`~torchrl.envs.transforms.RemoveEmptySpecs`. backend (str, optional): the backend. Can be either `"gym"` or `"gymnasium"` or any other backend compatible with :class:`~torchrl.envs.libs.gym.set_gym_backend`. to_numpy (bool, optional): if ``True``, the result of calls to `step` and `reset` will be mapped to numpy arrays. Defaults to ``False`` (results are tensors). This arg can be passed during a call to :func:`~gym.make` (see example below). reward_threshold (float, optional): [Gym kwarg] The reward threshold considered to have learnt an environment. nondeterministic (bool, optional): [Gym kwarg If the environment is nondeterministic (even with knowledge of the initial seed and all actions). Defaults to ``False``. max_episode_steps (int, optional): [Gym kwarg] The maximum number of episodes steps before truncation. Used by the Time Limit wrapper. order_enforce (bool, optional): [Gym >= 0.14] Whether the order enforcer wrapper should be applied to ensure users run functions in the correct order. Defaults to ``True``. autoreset (bool, optional): [Gym >= 0.14] Whether the autoreset wrapper should be added such that reset does not need to be called. Defaults to ``False``. disable_env_checker: [Gym >= 0.14] Whether the environment checker should be disabled for the environment. Defaults to ``False``. apply_api_compatibility: [Gym >= 0.26] If to apply the `StepAPICompatibility` wrapper. Defaults to ``False``. **kwargs: arbitrary keyword arguments which are passed to the environment constructor. .. note:: TorchRL's environment do not have the concept of an ``"info"`` dictionary, as ``TensorDict`` offers all the storage requirements deemed necessary in most training settings. Still, you can use the ``info_keys`` argument to have a fine grained control over what is deemed to be considered as an observation and what should be seen as info. Examples: >>> # Register the "cheetah" env from DMControl with the "run" task >>> from torchrl.envs import DMControlEnv >>> import torch >>> DMControlEnv.register_gym("DMC-cheetah-v0", to_numpy=False, backend="gym", env_name="cheetah", task_name="run") >>> import gym >>> envgym = gym.make("DMC-cheetah-v0") >>> envgym.seed(0) >>> torch.manual_seed(0) >>> envgym.reset() ({'position': tensor([-0.0855, 0.0215, -0.0881, -0.0412, -0.1101, 0.0080, 0.0254, 0.0424], dtype=torch.float64), 'velocity': tensor([ 1.9609e-02, -1.9776e-04, -1.6347e-03, 3.3842e-02, 2.5338e-02, 3.3064e-02, 1.0381e-04, 7.6656e-05, 1.0204e-02], dtype=torch.float64)}, {}) >>> envgym.step(envgym.action_space.sample()) ({'position': tensor([-0.0833, 0.0275, -0.0612, -0.0770, -0.1256, 0.0082, 0.0186, 0.0476], dtype=torch.float64), 'velocity': tensor([ 0.2221, 0.2256, 0.5930, 2.6937, -3.5865, -1.5479, 0.0187, -0.6825, 0.5224], dtype=torch.float64)}, tensor([0.0018], dtype=torch.float64), tensor([False]), tensor([False]), {}) >>> # same environment with observation stacked >>> from torchrl.envs import CatTensors >>> envgym = gym.make("DMC-cheetah-v0", transform=CatTensors(in_keys=["position", "velocity"], out_key="observation")) >>> envgym.reset() ({'observation': tensor([-0.1005, 0.0335, -0.0268, 0.0133, -0.0627, 0.0074, -0.0488, -0.0353, -0.0075, -0.0069, 0.0098, -0.0058, 0.0033, -0.0157, -0.0004, -0.0381, -0.0452], dtype=torch.float64)}, {}) >>> # same environment with numpy observations >>> envgym = gym.make("DMC-cheetah-v0", transform=CatTensors(in_keys=["position", "velocity"], out_key="observation"), to_numpy=True) >>> envgym.reset() ({'observation': array([-0.11355747, 0.04257728, 0.00408397, 0.04155852, -0.0389733 , -0.01409826, -0.0978704 , -0.08808327, 0.03970837, 0.00535434, -0.02353762, 0.05116226, 0.02788907, 0.06848346, 0.05154399, 0.0371798 , 0.05128025])}, {}) >>> # If gymnasium is installed, we can register the environment there too. >>> DMControlEnv.register_gym("DMC-cheetah-v0", to_numpy=False, backend="gymnasium", env_name="cheetah", task_name="run") >>> import gymnasium >>> envgym = gymnasium.make("DMC-cheetah-v0") >>> envgym.seed(0) >>> torch.manual_seed(0) >>> envgym.reset() ({'position': tensor([-0.0855, 0.0215, -0.0881, -0.0412, -0.1101, 0.0080, 0.0254, 0.0424], dtype=torch.float64), 'velocity': tensor([ 1.9609e-02, -1.9776e-04, -1.6347e-03, 3.3842e-02, 2.5338e-02, 3.3064e-02, 1.0381e-04, 7.6656e-05, 1.0204e-02], dtype=torch.float64)}, {}) .. note:: This feature also works for stateless environments (eg, :class:`~torchrl.envs.BraxEnv`). >>> import gymnasium >>> import torch >>> from tensordict import TensorDict >>> from torchrl.envs import BraxEnv, SelectTransform >>> >>> # get action for dydactic purposes >>> env = BraxEnv("ant", batch_size=[2]) >>> env.set_seed(0) >>> torch.manual_seed(0) >>> td = env.rollout(10) >>> >>> actions = td.get("action") >>> >>> # register env >>> env.register_gym("Brax-Ant-v0", env_name="ant", batch_size=[2], info_keys=["state"]) >>> gym_env = gymnasium.make("Brax-Ant-v0") >>> gym_env.seed(0) >>> torch.manual_seed(0) >>> >>> gym_env.reset() >>> obs = [] >>> for i in range(10): ... obs, reward, terminated, truncated, info = gym_env.step(td[..., i].get("action")) """ from torchrl.envs.libs.gym import gym_backend, set_gym_backend if backend is None: backend = gym_backend() with set_gym_backend(backend): return cls._register_gym( id=id, entry_point=entry_point, transform=transform, info_keys=info_keys, to_numpy=to_numpy, reward_threshold=reward_threshold, nondeterministic=nondeterministic, max_episode_steps=max_episode_steps, order_enforce=order_enforce, autoreset=autoreset, disable_env_checker=disable_env_checker, apply_api_compatibility=apply_api_compatibility, **kwargs, )
_GYM_UNRECOGNIZED_KWARG = ( "The keyword argument {} is not compatible with gym version {}" ) @implement_for("gym", "0.26", None, class_method=True) def _register_gym( cls, id, entry_point: Callable | None = None, transform: "Transform" | None = None, # noqa: F821 info_keys: List[NestedKey] | None = None, to_numpy: bool = False, reward_threshold: float | None = None, nondeterministic: bool = False, max_episode_steps: int | None = None, order_enforce: bool = True, autoreset: bool = False, disable_env_checker: bool = False, apply_api_compatibility: bool = False, **kwargs, ): import gym from torchrl.envs.libs._gym_utils import _TorchRLGymWrapper if entry_point is None: entry_point = cls entry_point = functools.partial( _TorchRLGymWrapper, entry_point=entry_point, info_keys=info_keys, to_numpy=to_numpy, transform=transform, **kwargs, ) return gym.register( id=id, entry_point=entry_point, reward_threshold=reward_threshold, nondeterministic=nondeterministic, max_episode_steps=max_episode_steps, order_enforce=order_enforce, autoreset=autoreset, disable_env_checker=disable_env_checker, apply_api_compatibility=apply_api_compatibility, ) @implement_for("gym", "0.25", "0.26", class_method=True) def _register_gym( # noqa: F811 cls, id, entry_point: Callable | None = None, transform: "Transform" | None = None, # noqa: F821 info_keys: List[NestedKey] | None = None, to_numpy: bool = False, reward_threshold: float | None = None, nondeterministic: bool = False, max_episode_steps: int | None = None, order_enforce: bool = True, autoreset: bool = False, disable_env_checker: bool = False, apply_api_compatibility: bool = False, **kwargs, ): import gym if apply_api_compatibility is not False: raise TypeError( cls._GYM_UNRECOGNIZED_KWARG.format( "apply_api_compatibility", gym.__version__ ) ) from torchrl.envs.libs._gym_utils import _TorchRLGymWrapper if entry_point is None: entry_point = cls entry_point = functools.partial( _TorchRLGymWrapper, entry_point=entry_point, info_keys=info_keys, to_numpy=to_numpy, transform=transform, **kwargs, ) return gym.register( id=id, entry_point=entry_point, reward_threshold=reward_threshold, nondeterministic=nondeterministic, max_episode_steps=max_episode_steps, order_enforce=order_enforce, autoreset=autoreset, disable_env_checker=disable_env_checker, ) @implement_for("gym", "0.24", "0.25", class_method=True) def _register_gym( # noqa: F811 cls, id, entry_point: Callable | None = None, transform: "Transform" | None = None, # noqa: F821 info_keys: List[NestedKey] | None = None, to_numpy: bool = False, reward_threshold: float | None = None, nondeterministic: bool = False, max_episode_steps: int | None = None, order_enforce: bool = True, autoreset: bool = False, disable_env_checker: bool = False, apply_api_compatibility: bool = False, **kwargs, ): import gym if apply_api_compatibility is not False: raise TypeError( cls._GYM_UNRECOGNIZED_KWARG.format( "apply_api_compatibility", gym.__version__ ) ) if disable_env_checker is not False: raise TypeError( cls._GYM_UNRECOGNIZED_KWARG.format( "disable_env_checker", gym.__version__ ) ) from torchrl.envs.libs._gym_utils import _TorchRLGymWrapper if entry_point is None: entry_point = cls entry_point = functools.partial( _TorchRLGymWrapper, entry_point=entry_point, info_keys=info_keys, to_numpy=to_numpy, transform=transform, **kwargs, ) return gym.register( id=id, entry_point=entry_point, reward_threshold=reward_threshold, nondeterministic=nondeterministic, max_episode_steps=max_episode_steps, order_enforce=order_enforce, autoreset=autoreset, ) @implement_for("gym", "0.21", "0.24", class_method=True) def _register_gym( # noqa: F811 cls, id, entry_point: Callable | None = None, transform: "Transform" | None = None, # noqa: F821 info_keys: List[NestedKey] | None = None, to_numpy: bool = False, reward_threshold: float | None = None, nondeterministic: bool = False, max_episode_steps: int | None = None, order_enforce: bool = True, autoreset: bool = False, disable_env_checker: bool = False, apply_api_compatibility: bool = False, **kwargs, ): import gym if apply_api_compatibility is not False: raise TypeError( cls._GYM_UNRECOGNIZED_KWARG.format( "apply_api_compatibility", gym.__version__ ) ) if disable_env_checker is not False: raise TypeError( cls._GYM_UNRECOGNIZED_KWARG.format( "disable_env_checker", gym.__version__ ) ) if autoreset is not False: raise TypeError( cls._GYM_UNRECOGNIZED_KWARG.format("autoreset", gym.__version__) ) from torchrl.envs.libs._gym_utils import _TorchRLGymWrapper if entry_point is None: entry_point = cls entry_point = functools.partial( _TorchRLGymWrapper, entry_point=entry_point, info_keys=info_keys, to_numpy=to_numpy, transform=transform, **kwargs, ) return gym.register( id=id, entry_point=entry_point, reward_threshold=reward_threshold, nondeterministic=nondeterministic, max_episode_steps=max_episode_steps, order_enforce=order_enforce, ) @implement_for("gym", None, "0.21", class_method=True) def _register_gym( # noqa: F811 cls, id, entry_point: Callable | None = None, transform: "Transform" | None = None, # noqa: F821 info_keys: List[NestedKey] | None = None, to_numpy: bool = False, reward_threshold: float | None = None, nondeterministic: bool = False, max_episode_steps: int | None = None, order_enforce: bool = True, autoreset: bool = False, disable_env_checker: bool = False, apply_api_compatibility: bool = False, **kwargs, ): import gym from torchrl.envs.libs._gym_utils import _TorchRLGymWrapper if order_enforce is not True: raise TypeError( cls._GYM_UNRECOGNIZED_KWARG.format("order_enforce", gym.__version__) ) if disable_env_checker is not False: raise TypeError( cls._GYM_UNRECOGNIZED_KWARG.format( "disable_env_checker", gym.__version__ ) ) if autoreset is not False: raise TypeError( cls._GYM_UNRECOGNIZED_KWARG.format("autoreset", gym.__version__) ) if apply_api_compatibility is not False: raise TypeError( cls._GYM_UNRECOGNIZED_KWARG.format( "apply_api_compatibility", gym.__version__ ) ) if entry_point is None: entry_point = cls entry_point = functools.partial( _TorchRLGymWrapper, entry_point=entry_point, info_keys=info_keys, to_numpy=to_numpy, transform=transform, **kwargs, ) return gym.register( id=id, entry_point=entry_point, reward_threshold=reward_threshold, nondeterministic=nondeterministic, max_episode_steps=max_episode_steps, ) @implement_for("gymnasium", class_method=True) def _register_gym( # noqa: F811 cls, id, entry_point: Callable | None = None, transform: "Transform" | None = None, # noqa: F821 info_keys: List[NestedKey] | None = None, to_numpy: bool = False, reward_threshold: float | None = None, nondeterministic: bool = False, max_episode_steps: int | None = None, order_enforce: bool = True, autoreset: bool = False, disable_env_checker: bool = False, apply_api_compatibility: bool = False, **kwargs, ): import gymnasium from torchrl.envs.libs._gym_utils import _TorchRLGymnasiumWrapper if entry_point is None: entry_point = cls entry_point = functools.partial( _TorchRLGymnasiumWrapper, entry_point=entry_point, info_keys=info_keys, to_numpy=to_numpy, transform=transform, **kwargs, ) return gymnasium.register( id=id, entry_point=entry_point, reward_threshold=reward_threshold, nondeterministic=nondeterministic, max_episode_steps=max_episode_steps, order_enforce=order_enforce, autoreset=autoreset, disable_env_checker=disable_env_checker, apply_api_compatibility=apply_api_compatibility, )
[docs] def forward(self, tensordict: TensorDictBase) -> TensorDictBase: raise NotImplementedError("EnvBase.forward is not implemented")
@abc.abstractmethod def _step( self, tensordict: TensorDictBase, ) -> TensorDictBase: raise NotImplementedError @abc.abstractmethod def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: raise NotImplementedError
[docs] def reset( self, tensordict: Optional[TensorDictBase] = None, **kwargs, ) -> TensorDictBase: """Resets the environment. As for step and _step, only the private method :obj:`_reset` should be overwritten by EnvBase subclasses. Args: tensordict (TensorDictBase, optional): tensordict to be used to contain the resulting new observation. In some cases, this input can also be used to pass argument to the reset function. kwargs (optional): other arguments to be passed to the native reset function. Returns: a tensordict (or the input tensordict, if any), modified in place with the resulting observations. """ if tensordict is not None: self._assert_tensordict_shape(tensordict) tensordict_reset = self._reset(tensordict, **kwargs) # We assume that this is done properly # if reset.device != self.device: # reset = reset.to(self.device, non_blocking=True) if tensordict_reset is tensordict: raise RuntimeError( "EnvBase._reset should return outplace changes to the input " "tensordict. Consider emptying the TensorDict first (e.g. tensordict.empty()) " "inside _reset before writing new tensors onto this new instance." ) if not isinstance(tensordict_reset, TensorDictBase): raise RuntimeError( f"env._reset returned an object of type {type(tensordict_reset)} but a TensorDict was expected." ) return self._reset_proc_data(tensordict, tensordict_reset)
def _reset_proc_data(self, tensordict, tensordict_reset): self._complete_done(self.full_done_spec, tensordict_reset) self._reset_check_done(tensordict, tensordict_reset) if tensordict is not None: return _update_during_reset(tensordict_reset, tensordict, self.reset_keys) return tensordict_reset def _reset_check_done(self, tensordict, tensordict_reset): """Checks the done status after reset. If _reset signals were passed, we check that the env is not done for these indices. We also check that the input tensordict contained ``"done"``s if the reset is partial and incomplete. """ # we iterate over (reset_key, (done_key, truncated_key)) and check that all # values where reset was true now have a done set to False. # If no reset was present, all done and truncated must be False for reset_key, done_key_group in zip(self.reset_keys, self.done_keys_groups): reset_value = ( tensordict.get(reset_key, default=None) if tensordict is not None else None ) if reset_value is not None: for done_key in done_key_group: done_val = tensordict_reset.get(done_key) if done_val[reset_value].any() and not self._allow_done_after_reset: raise RuntimeError( f"Env done entry '{done_key}' was (partially) True after reset on specified '_reset' dimensions. This is not allowed." ) if ( done_key not in tensordict.keys(True) and done_val[~reset_value].any() ): warnings.warn( f"A partial `'_reset'` key has been passed to `reset` ({reset_key}), " f"but the corresponding done_key ({done_key}) was not present in the input " f"tensordict. " f"This is discouraged, since the input tensordict should contain " f"all the data not being reset." ) # we set the done val to tensordict, to make sure that # _update_during_reset does not pad the value tensordict.set(done_key, done_val) elif not self._allow_done_after_reset: for done_key in done_key_group: if tensordict_reset.get(done_key).any(): raise RuntimeError( f"The done entry '{done_key}' was (partially) True after a call to reset() in env {self}." ) def numel(self) -> int: return prod(self.batch_size)
[docs] def set_seed( self, seed: Optional[int] = None, static_seed: bool = False ) -> Optional[int]: """Sets the seed of the environment and returns the next seed to be used (which is the input seed if a single environment is present). Args: seed (int): seed to be set static_seed (bool, optional): if ``True``, the seed is not incremented. Defaults to False Returns: integer representing the "next seed": i.e. the seed that should be used for another environment if created concomittently to this environment. """ if seed is not None: torch.manual_seed(seed) self._set_seed(seed) if seed is not None and not static_seed: new_seed = seed_generator(seed) seed = new_seed return seed
@abc.abstractmethod def _set_seed(self, seed: Optional[int]): raise NotImplementedError def set_state(self): raise NotImplementedError def _assert_tensordict_shape(self, tensordict: TensorDictBase) -> None: if ( self.batch_locked or self.batch_size != () ) and tensordict.batch_size != self.batch_size: raise RuntimeError( f"Expected a tensordict with shape==env.batch_size, " f"got {tensordict.batch_size} and {self.batch_size}" )
[docs] def rand_action(self, tensordict: Optional[TensorDictBase] = None): """Performs a random action given the action_spec attribute. Args: tensordict (TensorDictBase, optional): tensordict where the resulting action should be written. Returns: a tensordict object with the "action" entry updated with a random sample from the action-spec. """ shape = torch.Size([]) if not self.batch_locked: if not self.batch_size and tensordict is not None: # if we can't infer the batch-size from the env, take it from tensordict shape = tensordict.shape elif not self.batch_size: # if tensordict wasn't provided, we assume empty batch size shape = torch.Size([]) elif tensordict.shape != self.batch_size: # if tensordict is not None and the env has a batch size, their shape must match raise RuntimeError( "The input tensordict and the env have a different batch size: " f"env.batch_size={self.batch_size} and tensordict.batch_size={tensordict.shape}. " f"Non batch-locked environment require the env batch-size to be either empty or to" f" match the tensordict one." ) # We generate the action from the full_action_spec r = self.input_spec["full_action_spec"].rand(shape) if tensordict is None: return r tensordict.update(r) return tensordict
[docs] def rand_step(self, tensordict: Optional[TensorDictBase] = None) -> TensorDictBase: """Performs a random step in the environment given the action_spec attribute. Args: tensordict (TensorDictBase, optional): tensordict where the resulting info should be written. Returns: a tensordict object with the new observation after a random step in the environment. The action will be stored with the "action" key. """ tensordict = self.rand_action(tensordict) return self.step(tensordict)
@property def specs(self) -> CompositeSpec: """Returns a Composite container where all the environment are present. This feature allows one to create an environment, retrieve all of the specs in a single data container and then erase the environment from the workspace. """ return CompositeSpec( output_spec=self.output_spec, input_spec=self.input_spec, shape=self.batch_size, ).lock_()
[docs] def rollout( self, max_steps: int, policy: Optional[Callable[[TensorDictBase], TensorDictBase]] = None, callback: Optional[Callable[[TensorDictBase, ...], Any]] = None, auto_reset: bool = True, auto_cast_to_device: bool = False, break_when_any_done: bool = True, return_contiguous: bool = True, tensordict: Optional[TensorDictBase] = None, set_truncated: bool = False, out=None, ): """Executes a rollout in the environment. The function will stop as soon as one of the contained environments returns done=True. Args: max_steps (int): maximum number of steps to be executed. The actual number of steps can be smaller if the environment reaches a done state before max_steps have been executed. policy (callable, optional): callable to be called to compute the desired action. If no policy is provided, actions will be called using :obj:`env.rand_step()`. The policy can be any callable that reads either a tensordict or the entire sequence of observation entries __sorted as__ the ``env.observation_spec.keys()``. Defaults to `None`. callback (Callable[[TensorDict], Any], optional): function to be called at each iteration with the given TensorDict. Defaults to ``None``. The output of ``callback`` will not be collected, it is the user responsibility to save any result within the callback call if data needs to be carried over beyond the call to ``rollout``. auto_reset (bool, optional): if ``True``, resets automatically the environment if it is in a done state when the rollout is initiated. Default is ``True``. auto_cast_to_device (bool, optional): if ``True``, the device of the tensordict is automatically cast to the policy device before the policy is used. Default is ``False``. break_when_any_done (bool): breaks if any of the done state is True. If False, a reset() is called on the sub-envs that are done. Default is True. return_contiguous (bool): if False, a LazyStackedTensorDict will be returned. Default is True. tensordict (TensorDict, optional): if auto_reset is False, an initial tensordict must be provided. Rollout will check if this tensordict has done flags and reset the environment in those dimensions (if needed). This normally should not occur if ``tensordict`` is the output of a reset, but can occur if ``tensordict`` is the last step of a previous rollout. set_truncated (bool, optional): if ``True``, ``"truncated"`` and ``"done"`` keys will be set to ``True`` after completion of the rollout. If no ``"truncated"`` is found within the ``done_spec``, an exception is raised. Truncated keys can be set through ``env.add_truncated_keys``. Defaults to ``False``. Returns: TensorDict object containing the resulting trajectory. The data returned will be marked with a "time" dimension name for the last dimension of the tensordict (at the ``env.ndim`` index). ``rollout`` is quite handy to display what the data structure of the environment looks like. Examples: >>> # Using rollout without a policy >>> from torchrl.envs.libs.gym import GymEnv >>> from torchrl.envs.transforms import TransformedEnv, StepCounter >>> env = TransformedEnv(GymEnv("Pendulum-v1"), StepCounter(max_steps=20)) >>> rollout = env.rollout(max_steps=1000) >>> print(rollout) TensorDict( fields={ action: Tensor(shape=torch.Size([20, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([20, 1]), device=cpu, dtype=torch.bool, is_shared=False), next: TensorDict( fields={ done: Tensor(shape=torch.Size([20, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: Tensor(shape=torch.Size([20, 3]), device=cpu, dtype=torch.float32, is_shared=False), reward: Tensor(shape=torch.Size([20, 1]), device=cpu, dtype=torch.float32, is_shared=False), step_count: Tensor(shape=torch.Size([20, 1]), device=cpu, dtype=torch.int64, is_shared=False), truncated: Tensor(shape=torch.Size([20, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([20]), device=cpu, is_shared=False), observation: Tensor(shape=torch.Size([20, 3]), device=cpu, dtype=torch.float32, is_shared=False), step_count: Tensor(shape=torch.Size([20, 1]), device=cpu, dtype=torch.int64, is_shared=False), truncated: Tensor(shape=torch.Size([20, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([20]), device=cpu, is_shared=False) >>> print(rollout.names) ['time'] >>> # with envs that contain more dimensions >>> from torchrl.envs import SerialEnv >>> env = SerialEnv(3, lambda: TransformedEnv(GymEnv("Pendulum-v1"), StepCounter(max_steps=20))) >>> rollout = env.rollout(max_steps=1000) >>> print(rollout) TensorDict( fields={ action: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.bool, is_shared=False), next: TensorDict( fields={ done: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: Tensor(shape=torch.Size([3, 20, 3]), device=cpu, dtype=torch.float32, is_shared=False), reward: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.float32, is_shared=False), step_count: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.int64, is_shared=False), truncated: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([3, 20]), device=cpu, is_shared=False), observation: Tensor(shape=torch.Size([3, 20, 3]), device=cpu, dtype=torch.float32, is_shared=False), step_count: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.int64, is_shared=False), truncated: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([3, 20]), device=cpu, is_shared=False) >>> print(rollout.names) [None, 'time'] Using a policy (a regular :class:`~torch.nn.Module` or a :class:`~tensordict.nn.TensorDictModule`) is also easy: Examples: >>> from torch import nn >>> env = GymEnv("CartPole-v1", categorical_action_encoding=True) >>> class ArgMaxModule(nn.Module): ... def forward(self, values): ... return values.argmax(-1) >>> n_obs = env.observation_spec["observation"].shape[-1] >>> n_act = env.action_spec.n >>> # A deterministic policy >>> policy = nn.Sequential( ... nn.Linear(n_obs, n_act), ... ArgMaxModule()) >>> env.rollout(max_steps=10, policy=policy) TensorDict( fields={ action: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.int64, is_shared=False), done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), next: TensorDict( fields={ done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: Tensor(shape=torch.Size([10, 4]), device=cpu, dtype=torch.float32, is_shared=False), reward: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), truncated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([10]), device=cpu, is_shared=False), observation: Tensor(shape=torch.Size([10, 4]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), truncated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([10]), device=cpu, is_shared=False) >>> # Under the hood, rollout will wrap the policy in a TensorDictModule >>> # To speed things up we can do that ourselves >>> from tensordict.nn import TensorDictModule >>> policy = TensorDictModule(policy, in_keys=list(env.observation_spec.keys()), out_keys=["action"]) >>> env.rollout(max_steps=10, policy=policy) TensorDict( fields={ action: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.int64, is_shared=False), done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), next: TensorDict( fields={ done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: Tensor(shape=torch.Size([10, 4]), device=cpu, dtype=torch.float32, is_shared=False), reward: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), truncated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([10]), device=cpu, is_shared=False), observation: Tensor(shape=torch.Size([10, 4]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), truncated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([10]), device=cpu, is_shared=False) In some instances, contiguous tensordict cannot be obtained because they cannot be stacked. This can happen when the data returned at each step may have a different shape, or when different environments are executed together. In that case, ``return_contiguous=False`` will cause the returned tensordict to be a lazy stack of tensordicts: Examples of non-contiguous rollout: >>> rollout = env.rollout(4, return_contiguous=False) >>> print(rollout) LazyStackedTensorDict( fields={ action: Tensor(shape=torch.Size([3, 4, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([3, 4, 1]), device=cpu, dtype=torch.bool, is_shared=False), next: LazyStackedTensorDict( fields={ done: Tensor(shape=torch.Size([3, 4, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: Tensor(shape=torch.Size([3, 4, 3]), device=cpu, dtype=torch.float32, is_shared=False), reward: Tensor(shape=torch.Size([3, 4, 1]), device=cpu, dtype=torch.float32, is_shared=False), step_count: Tensor(shape=torch.Size([3, 4, 1]), device=cpu, dtype=torch.int64, is_shared=False), truncated: Tensor(shape=torch.Size([3, 4, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([3, 4]), device=cpu, is_shared=False), observation: Tensor(shape=torch.Size([3, 4, 3]), device=cpu, dtype=torch.float32, is_shared=False), step_count: Tensor(shape=torch.Size([3, 4, 1]), device=cpu, dtype=torch.int64, is_shared=False), truncated: Tensor(shape=torch.Size([3, 4, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([3, 4]), device=cpu, is_shared=False) >>> print(rollout.names) [None, 'time'] Rollouts can be used in a loop to emulate data collection. To do so, you need to pass as input the last tensordict coming from the previous rollout after calling :func:`~torchrl.envs.utils.step_mdp` on it. Examples of data collection rollouts: >>> from torchrl.envs import GymEnv, step_mdp >>> env = GymEnv("CartPole-v1") >>> epochs = 10 >>> input_td = env.reset() >>> for i in range(epochs): ... rollout_td = env.rollout( ... max_steps=100, ... break_when_any_done=False, ... auto_reset=False, ... tensordict=input_td, ... ) ... input_td = step_mdp( ... rollout_td[..., -1], ... ) """ if policy is not None: policy = _make_compatible_policy( policy, self.observation_spec, env=self, fast_wrap=True ) if auto_cast_to_device: try: policy_device = next(policy.parameters()).device except (StopIteration, AttributeError): policy_device = None else: policy_device = None else: policy = self.rand_action policy_device = None env_device = self.device if auto_reset: if tensordict is not None: raise RuntimeError( "tensordict cannot be provided when auto_reset is True" ) tensordict = self.reset() elif tensordict is None: raise RuntimeError("tensordict must be provided when auto_reset is False") else: tensordict = self.maybe_reset(tensordict) kwargs = { "tensordict": tensordict, "auto_cast_to_device": auto_cast_to_device, "max_steps": max_steps, "policy": policy, "policy_device": policy_device, "env_device": env_device, "callback": callback, } if break_when_any_done: tensordicts = self._rollout_stop_early(**kwargs) else: tensordicts = self._rollout_nonstop(**kwargs) batch_size = self.batch_size if tensordict is None else tensordict.batch_size if return_contiguous: out_td = torch.stack(tensordicts, len(batch_size), out=out) else: out_td = LazyStackedTensorDict.lazy_stack( tensordicts, len(batch_size), out=out ) if set_truncated: found_truncated = False for key in self.done_keys: if _ends_with(key, "truncated"): val = out_td.get(("next", key)) val[(slice(None),) * (out_td.ndim - 1) + (-1,)] = True out_td.set(("next", key), val) out_td.set(("next", _replace_last(key, "done")), val) found_truncated = True if not found_truncated: raise RuntimeError( "set_truncated was set to True but no truncated key could be found. " "Make sure a 'truncated' entry was set in the environment " "full_done_keys using `env.add_truncated_keys()`." ) out_td.refine_names(..., "time") return out_td
[docs] def add_truncated_keys(self) -> EnvBase: """Adds truncated keys to the environment.""" for key in self.done_keys: self.full_done_spec[_replace_last(key, "truncated")] = self.full_done_spec[ key ] self.__dict__["_done_keys"] = None return self
@property def _step_mdp(self): step_func = self.__dict__.get("_step_mdp_value", None) if step_func is None: step_func = _StepMDP(self, exclude_action=False) self.__dict__["_step_mdp_value"] = step_func return step_func def _rollout_stop_early( self, *, tensordict, auto_cast_to_device, max_steps, policy, policy_device, env_device, callback, ): # Get the sync func if auto_cast_to_device: sync_func = _get_sync_func(policy_device, env_device) tensordicts = [] for i in range(max_steps): if auto_cast_to_device: if policy_device is not None: tensordict = tensordict.to(policy_device, non_blocking=True) sync_func() else: tensordict.clear_device_() tensordict = policy(tensordict) if auto_cast_to_device: if env_device is not None: tensordict = tensordict.to(env_device, non_blocking=True) sync_func() else: tensordict.clear_device_() tensordict = self.step(tensordict) td_append = tensordict.copy() tensordicts.append(td_append) if i == max_steps - 1: # we don't truncate as one could potentially continue the run break tensordict = self._step_mdp(tensordict) # done and truncated are in done_keys # We read if any key is done. any_done = _terminated_or_truncated( tensordict, full_done_spec=self.output_spec["full_done_spec"], key=None, ) if any_done: break if callback is not None: callback(self, tensordict) return tensordicts def _rollout_nonstop( self, *, tensordict, auto_cast_to_device, max_steps, policy, policy_device, env_device, callback, ): if auto_cast_to_device: sync_func = _get_sync_func(policy_device, env_device) tensordicts = [] tensordict_ = tensordict for i in range(max_steps): if auto_cast_to_device: if policy_device is not None: tensordict_ = tensordict_.to(policy_device, non_blocking=True) sync_func() else: tensordict_.clear_device_() tensordict_ = policy(tensordict_) if auto_cast_to_device: if env_device is not None: tensordict_ = tensordict_.to(env_device, non_blocking=True) sync_func() else: tensordict_.clear_device_() if i == max_steps - 1: tensordict = self.step(tensordict_) else: tensordict, tensordict_ = self.step_and_maybe_reset(tensordict_) tensordicts.append(tensordict) if i == max_steps - 1: # we don't truncate as one could potentially continue the run break if callback is not None: callback(self, tensordict) return tensordicts
[docs] def step_and_maybe_reset( self, tensordict: TensorDictBase ) -> Tuple[TensorDictBase, TensorDictBase]: """Runs a step in the environment and (partially) resets it if needed. Args: tensordict (TensorDictBase): an input data structure for the :meth:`~.step` method. This method allows to easily code non-stopping rollout functions. Examples: >>> from torchrl.envs import ParallelEnv, GymEnv >>> def rollout(env, n): ... data_ = env.reset() ... result = [] ... for i in range(n): ... data, data_ = env.step_and_maybe_reset(data_) ... result.append(data) ... return torch.stack(result) >>> env = ParallelEnv(2, lambda: GymEnv("CartPole-v1")) >>> print(rollout(env, 2)) TensorDict( fields={ done: Tensor(shape=torch.Size([2, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False), next: TensorDict( fields={ done: Tensor(shape=torch.Size([2, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: Tensor(shape=torch.Size([2, 2, 4]), device=cpu, dtype=torch.float32, is_shared=False), reward: Tensor(shape=torch.Size([2, 2, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([2, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False), truncated: Tensor(shape=torch.Size([2, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([2, 2]), device=cpu, is_shared=False), observation: Tensor(shape=torch.Size([2, 2, 4]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([2, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False), truncated: Tensor(shape=torch.Size([2, 2, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([2, 2]), device=cpu, is_shared=False) """ tensordict = self.step(tensordict) # done and truncated are in done_keys # We read if any key is done. tensordict_ = self._step_mdp(tensordict) tensordict_ = self.maybe_reset(tensordict_) return tensordict, tensordict_
@property def _simple_done(self): _simple_done = self.__dict__.get("_simple_done_value", None) if _simple_done is None: key_set = set(self.full_done_spec.keys()) _simple_done = key_set == { "done", "truncated", "terminated", } or key_set == {"done", "terminated"} self.__dict__["_simple_done_value"] = _simple_done return _simple_done
[docs] def maybe_reset(self, tensordict: TensorDictBase) -> TensorDictBase: """Checks the done keys of the input tensordict and, if needed, resets the environment where it is done. Args: tensordict (TensorDictBase): a tensordict coming from the output of :func:`~torchrl.envs.utils.step_mdp`. Returns: A tensordict that is identical to the input where the environment was not reset and contains the new reset data where the environment was reset. """ if self._simple_done: done = tensordict._get_str("done", default=None) any_done = done.any() if any_done: tensordict._set_str( "_reset", done.clone(), validated=True, inplace=False, non_blocking=False, ) else: any_done = _terminated_or_truncated( tensordict, full_done_spec=self.output_spec["full_done_spec"], key="_reset", ) if any_done: tensordict = self.reset(tensordict) return tensordict
[docs] def empty_cache(self): """Erases all the cached values. For regular envs, the key lists (reward, done etc) are cached, but in some cases they may change during the execution of the code (eg, when adding a transform). """ self.__dict__["_step_mdp_value"] = None self.__dict__["_reward_keys"] = None self.__dict__["_done_keys"] = None self.__dict__["_action_keys"] = None self.__dict__["_done_keys_group"] = None
@property def reset_keys(self) -> List[NestedKey]: """Returns a list of reset keys. Reset keys are keys that indicate partial reset, in batched, multitask or multiagent settings. They are structured as ``(*prefix, "_reset")`` where ``prefix`` is a (possibly empty) tuple of strings pointing to a tensordict location where a done state can be found. Keys are sorted by depth in the data tree. """ reset_keys = self.__dict__.get("_reset_keys", None) if reset_keys is not None: return reset_keys reset_keys = sorted( ( _replace_last(done_key, "_reset") for (done_key, *_) in self.done_keys_groups ), key=_repr_by_depth, ) self.__dict__["_reset_keys"] = reset_keys return reset_keys @property def _filtered_reset_keys(self): """Returns only the effective reset keys, discarding nested resets if they're not being used.""" reset_keys = self.reset_keys result = [] def _root(key): if isinstance(key, str): return () return key[:-1] roots = [] for reset_key in reset_keys: cur_root = _root(reset_key) for root in roots: if cur_root[: len(root)] == root: break else: roots.append(cur_root) result.append(reset_key) return result @property def done_keys_groups(self): """A list of done keys, grouped as the reset keys. This is a list of lists. The outer list has the length of reset keys, the inner lists contain the done keys (eg, done and truncated) that can be read to determine a reset when it is absent. """ done_keys_group = self.__dict__.get("_done_keys_group", None) if done_keys_group is not None: return done_keys_group # done keys, sorted as reset keys done_keys_group = [] roots = set() fds = self.full_done_spec for done_key in self.done_keys: root_name = done_key[:-1] if isinstance(done_key, tuple) else () root = fds[root_name] if root_name else fds n = len(roots) roots.add(root_name) if len(roots) - n: done_keys_group.append( [ unravel_key(root_name + (key,)) for key in root.keys(include_nested=False, leaves_only=True) ] ) self.__dict__["_done_keys_group"] = done_keys_group return done_keys_group def _select_observation_keys(self, tensordict: TensorDictBase) -> Iterator[str]: for key in tensordict.keys(): if key.rfind("observation") >= 0: yield key def close(self): self.is_closed = True def __del__(self): # if del occurs before env has been set up, we don't want a recursion # error if "is_closed" in self.__dict__ and not self.is_closed: try: self.close() except Exception: # a TypeError will typically be raised if the env is deleted when the program ends. # In the future, insignificant changes to the close method may change the error type. # We excplicitely assume that any error raised during closure in # __del__ will not affect the program. pass
[docs] def to(self, device: DEVICE_TYPING) -> EnvBase: device = torch.device(device) if device == self.device: return self self.__dict__["_input_spec"] = self.input_spec.to(device).lock_() self.__dict__["_output_spec"] = self.output_spec.to(device).lock_() self._device = device return super().to(device)
[docs] def fake_tensordict(self) -> TensorDictBase: """Returns a fake tensordict with key-value pairs that match in shape, device and dtype what can be expected during an environment rollout.""" state_spec = self.state_spec observation_spec = self.observation_spec action_spec = self.input_spec["full_action_spec"] # instantiates reward_spec if needed _ = self.reward_spec reward_spec = self.output_spec["full_reward_spec"] full_done_spec = self.output_spec["full_done_spec"] fake_obs = observation_spec.zero() fake_state = state_spec.zero() fake_action = action_spec.zero() if any( isinstance(val, LazyStackedTensorDict) for val in fake_action.values(True) ): fake_input = fake_action.update(fake_state) else: fake_input = fake_state.update(fake_action) # the input and output key may match, but the output prevails # Hence we generate the input, and override using the output fake_in_out = fake_input.update(fake_obs) fake_reward = reward_spec.zero() fake_done = full_done_spec.zero() next_output = fake_obs.clone() next_output.update(fake_reward) next_output.update(fake_done) fake_in_out.update(fake_done.clone()) if "next" not in fake_in_out.keys(): fake_in_out.set("next", next_output) else: fake_in_out.get("next").update(next_output) fake_in_out.batch_size = self.batch_size fake_in_out = fake_in_out.to(self.device) return fake_in_out
class _EnvWrapper(EnvBase): """Abstract environment wrapper class. Unlike EnvBase, _EnvWrapper comes with a :obj:`_build_env` private method that will be called upon instantiation. Interfaces with other libraries should be coded using _EnvWrapper. It is possible to directly query attributed from the nested environment it its name does not conflict with an attribute of the wrapper: >>> env = SomeWrapper(...) >>> custom_attribute0 = env._env.custom_attribute >>> custom_attribute1 = env.custom_attribute >>> assert custom_attribute0 is custom_attribute1 # should return True """ git_url: str = "" available_envs: Dict[str, Any] = {} libname: str = "" def __init__( self, *args, device: DEVICE_TYPING = NO_DEFAULT, batch_size: Optional[torch.Size] = None, allow_done_after_reset: bool = False, **kwargs, ): if device is NO_DEFAULT: warnings.warn( "Your wrapper was not given a device. Currently, this " "value will default to 'cpu'. From v0.5 it will " "default to `None`. With a device of None, no device casting " "is performed and the resulting tensordicts are deviceless. " "Please set your device accordingly.", category=DeprecationWarning, ) device = torch.device("cpu") super().__init__( device=device, batch_size=batch_size, allow_done_after_reset=allow_done_after_reset, ) if len(args): raise ValueError( "`_EnvWrapper.__init__` received a non-empty args list of arguments." "Make sure only keywords arguments are used when calling `super().__init__`." ) frame_skip = kwargs.get("frame_skip", 1) if "frame_skip" in kwargs: del kwargs["frame_skip"] self.frame_skip = frame_skip # this value can be changed if frame_skip is passed during env construction self.wrapper_frame_skip = frame_skip self._constructor_kwargs = kwargs self._check_kwargs(kwargs) self._env = self._build_env(**kwargs) # writes the self._env attribute self._make_specs(self._env) # writes the self._env attribute self.is_closed = False self._init_env() # runs all the steps to have a ready-to-use env def _sync_device(self): sync_func = self.__dict__.get("_sync_device_val", None) if sync_func is None: device = self.device if device.type != "cuda": if torch.cuda.is_available(): self._sync_device_val = torch.cuda.synchronize elif torch.backends.mps.is_available(): self._sync_device_val = torch.cuda.synchronize elif device.type == "cpu": self._sync_device_val = _do_nothing else: self._sync_device_val = _do_nothing return self._sync_device return sync_func @abc.abstractmethod def _check_kwargs(self, kwargs: Dict): raise NotImplementedError def __getattr__(self, attr: str) -> Any: if attr in self.__dir__(): return self.__getattribute__( attr ) # make sure that appropriate exceptions are raised elif attr.startswith("__"): raise AttributeError( "passing built-in private methods is " f"not permitted with type {type(self)}. " f"Got attribute {attr}." ) elif "_env" in self.__dir__(): env = self.__getattribute__("_env") return getattr(env, attr) super().__getattr__(attr) raise AttributeError( f"env not set in {self.__class__.__name__}, cannot access {attr}" ) @abc.abstractmethod def _init_env(self) -> Optional[int]: """Runs all the necessary steps such that the environment is ready to use. This step is intended to ensure that a seed is provided to the environment (if needed) and that the environment is reset (if needed). For instance, DMControl envs require the env to be reset before being used, but Gym envs don't. Returns: the resulting seed """ raise NotImplementedError @abc.abstractmethod def _build_env(self, **kwargs) -> "gym.Env": # noqa: F821 """Creates an environment from the target library and stores it with the `_env` attribute. When overwritten, this function should pass all the required kwargs to the env instantiation method. """ raise NotImplementedError @abc.abstractmethod def _make_specs(self, env: "gym.Env") -> None: # noqa: F821 raise NotImplementedError def close(self) -> None: """Closes the contained environment if possible.""" self.is_closed = True try: self._env.close() except AttributeError: pass def make_tensordict( env: _EnvWrapper, policy: Optional[Callable[[TensorDictBase, ...], TensorDictBase]] = None, ) -> TensorDictBase: """Returns a zeroed-tensordict with fields matching those required for a full step (action selection and environment step) in the environment. Args: env (_EnvWrapper): environment defining the observation, action and reward space; policy (Callable, optional): policy corresponding to the environment. """ with torch.no_grad(): tensordict = env.reset() if policy is not None: tensordict = policy(tensordict) else: tensordict.set("action", env.action_spec.rand(), inplace=False) tensordict = env.step(tensordict) return tensordict.zero_() def _get_sync_func(policy_device, env_device): if torch.cuda.is_available(): # Look for a specific device if policy_device is not None and policy_device.type == "cuda": if env_device is None or env_device.type == "cuda": return torch.cuda.synchronize return functools.partial(torch.cuda.synchronize, device=policy_device) if env_device is not None and env_device.type == "cuda": if policy_device is None: return torch.cuda.synchronize return functools.partial(torch.cuda.synchronize, device=env_device) return torch.cuda.synchronize if torch.backends.mps.is_available(): return torch.mps.synchronize return _do_nothing def _do_nothing(): return

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