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Add support for python 3.6 (#2836)

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* Add support for python 3.6

* Add support for python 3.6

* Added check for python 3.6 to not install mujoco as no version exists

* Fixed the install groups for python 3.6

* Re-added python 3.6 support for gym

* black

* Added support for dataclasses through dataclasses module in setup that backports the module

* Fixed install requirements

* Re-added dummy env spec with dataclasses

* Changed type for compatability for python 3.6

* Added a python 3.6 warning

* Fixed python 3.6 typing issue

* Removed __future__ import annotation for python 3.6 support

* Fixed python 3.6 typing
This commit is contained in:
Mark Towers
2022-05-25 15:28:19 +01:00
committed by GitHub
parent 273e3f22ce
commit 0263deb5ab
27 changed files with 148 additions and 154 deletions
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2
.github/workflows/build.yml vendored
View File

@@ -6,7 +6,7 @@ jobs:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ['3.7', '3.8', '3.9', '3.10']
python-version: ['3.6', '3.7', '3.8', '3.9', '3.10']
steps:
- uses: actions/checkout@v2
- run: |

2
.pre-commit-config.yaml
View File

@@ -41,7 +41,7 @@ repos:
hooks:
- id: pyupgrade
# TODO: remove `--keep-runtime-typing` option
args: ["--py37-plus", "--keep-runtime-typing"]
args: ["--py36-plus", "--keep-runtime-typing"]
- repo: local
hooks:
- id: pyright

28
gym/core.py
View File

@@ -1,13 +1,17 @@
"""Core API for Environment, Wrapper, ActionWrapper, RewardWrapper and ObservationWrapper."""
from __future__ import annotations
from typing import Generic, Optional, SupportsFloat, TypeVar, Union
import sys
from typing import Generic, Optional, SupportsFloat, Tuple, TypeVar, Union
from gym import spaces
from gym.logger import deprecation
from gym.logger import deprecation, warn
from gym.utils import seeding
from gym.utils.seeding import RandomNumberGenerator
if sys.version_info == (3, 6):
warn(
"Gym minimally supports python 3.6 as the python foundation not longer supports the version, please update your version to 3.7+"
)
ObsType = TypeVar("ObsType")
ActType = TypeVar("ActType")
@@ -62,7 +66,7 @@ class Env(Generic[ObsType, ActType]):
def np_random(self, value: RandomNumberGenerator):
self._np_random = value
def step(self, action: ActType) -> tuple[ObsType, float, bool, dict]:
def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
"""Run one timestep of the environment's dynamics.
When end of episode is reached, you are responsible for calling :meth:`reset` to reset this environment's state.
@@ -92,7 +96,7 @@ class Env(Generic[ObsType, ActType]):
seed: Optional[int] = None,
return_info: bool = False,
options: Optional[dict] = None,
) -> Union[ObsType, tuple[ObsType, dict]]:
) -> Union[ObsType, Tuple[ObsType, dict]]:
"""Resets the environment to an initial state and returns the initial observation.
This method can reset the environment's random number generator(s) if ``seed`` is an integer or
@@ -201,7 +205,7 @@ class Env(Generic[ObsType, ActType]):
return [seed]
@property
def unwrapped(self) -> Env:
def unwrapped(self) -> "Env":
"""Returns the base non-wrapped environment.
Returns:
@@ -248,7 +252,7 @@ class Wrapper(Env[ObsType, ActType]):
self._action_space: Optional[spaces.Space] = None
self._observation_space: Optional[spaces.Space] = None
self._reward_range: Optional[tuple[SupportsFloat, SupportsFloat]] = None
self._reward_range: Optional[Tuple[SupportsFloat, SupportsFloat]] = None
self._metadata: Optional[dict] = None
def __getattr__(self, name):
@@ -290,14 +294,14 @@ class Wrapper(Env[ObsType, ActType]):
self._observation_space = space
@property
def reward_range(self) -> tuple[SupportsFloat, SupportsFloat]:
def reward_range(self) -> Tuple[SupportsFloat, SupportsFloat]:
"""Return the reward range of the environment."""
if self._reward_range is None:
return self.env.reward_range
return self._reward_range
@reward_range.setter
def reward_range(self, value: tuple[SupportsFloat, SupportsFloat]):
def reward_range(self, value: Tuple[SupportsFloat, SupportsFloat]):
self._reward_range = value
@property
@@ -311,11 +315,11 @@ class Wrapper(Env[ObsType, ActType]):
def metadata(self, value):
self._metadata = value
def step(self, action: ActType) -> tuple[ObsType, float, bool, dict]:
def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
"""Steps through the environment with action."""
return self.env.step(action)
def reset(self, **kwargs) -> Union[ObsType, tuple[ObsType, dict]]:
def reset(self, **kwargs) -> Union[ObsType, Tuple[ObsType, dict]]:
"""Resets the environment with kwargs."""
return self.env.reset(**kwargs)

46
gym/envs/registration.py
View File

@@ -1,5 +1,3 @@
from __future__ import annotations
import contextlib
import copy
import difflib
@@ -10,12 +8,14 @@ import sys
import warnings
from dataclasses import dataclass, field
from typing import (
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Sequence,
SupportsFloat,
Tuple,
Union,
overload,
)
@@ -34,15 +34,11 @@ else:
if sys.version_info >= (3, 8):
from typing import Literal
else:
class Literal(str):
def __class_getitem__(cls, item):
return Any
from typing_extensions import Literal
from gym import Env, error, logger
ENV_ID_RE: re.Pattern = re.compile(
ENV_ID_RE = re.compile(
r"^(?:(?P<namespace>[\w:-]+)\/)?(?:(?P<name>[\w:.-]+?))(?:-v(?P<version>\d+))?$"
)
@@ -54,7 +50,7 @@ def load(name: str) -> type:
return fn
def parse_env_id(id: str) -> tuple[Optional[str], str, Optional[int]]:
def parse_env_id(id: str) -> Tuple[Optional[str], str, Optional[int]]:
"""Parse environment ID string format.
This format is true today, but it's *not* an official spec.
@@ -241,7 +237,7 @@ def _check_version_exists(ns: Optional[str], name: str, version: Optional[int]):
def find_highest_version(ns: Optional[str], name: str) -> Optional[int]:
version: list[int] = [
version: List[int] = [
spec_.version
for spec_ in registry.values()
if spec_.namespace == ns and spec_.name == name and spec_.version is not None
@@ -302,39 +298,39 @@ def load_env_plugins(entry_point: str = "gym.envs") -> None:
@overload
def make(id: Literal["CartPole-v1"], **kwargs) -> Env[np.ndarray, np.ndarray | int]: ...
def make(id: Literal["CartPole-v0", "CartPole-v1"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, int]]: ...
@overload
def make(id: Literal["MountainCar-v0"], **kwargs) -> Env[np.ndarray, np.ndarray | int]: ...
def make(id: Literal["MountainCar-v0"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, int]]: ...
@overload
def make(id: Literal["MountainCarContinuous-v0"], **kwargs) -> Env[np.ndarray, np.ndarray | Sequence[SupportsFloat]]: ...
def make(id: Literal["MountainCarContinuous-v0"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, Sequence[SupportsFloat]]]: ...
@overload
def make(id: Literal["Pendulum-v1"], **kwargs) -> Env[np.ndarray, np.ndarray | Sequence[SupportsFloat]]: ...
def make(id: Literal["Pendulum-v1"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, Sequence[SupportsFloat]]]: ...
@overload
def make(id: Literal["Acrobot-v1"], **kwargs) -> Env[np.ndarray, np.ndarray | int]: ...
def make(id: Literal["Acrobot-v1"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, int]]: ...
# Box2d
# ----------------------------------------
@overload
def make(id: Literal["LunarLander-v2", "LunarLanderContinuous-v2"], **kwargs) -> Env[np.ndarray, np.ndarray | int]: ...
def make(id: Literal["LunarLander-v2", "LunarLanderContinuous-v2"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, int]]: ...
@overload
def make(id: Literal["BipedalWalker-v3", "BipedalWalkerHardcore-v3"], **kwargs) -> Env[np.ndarray, np.ndarray | Sequence[SupportsFloat]]: ...
def make(id: Literal["BipedalWalker-v3", "BipedalWalkerHardcore-v3"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, Sequence[SupportsFloat]]]: ...
@overload
def make(id: Literal["CarRacing-v1", "CarRacingDomainRandomize-v1"], **kwargs) -> Env[np.ndarray, np.ndarray | Sequence[SupportsFloat]]: ...
def make(id: Literal["CarRacing-v1", "CarRacingDomainRandomize-v1"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, Sequence[SupportsFloat]]]: ...
# Toy Text
# ----------------------------------------
@overload
def make(id: Literal["Blackjack-v1"], **kwargs) -> Env[np.ndarray, np.ndarray | int]: ...
def make(id: Literal["Blackjack-v1"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, int]]: ...
@overload
def make(id: Literal["FrozenLake-v1", "FrozenLake8x8-v1"], **kwargs) -> Env[np.ndarray, np.ndarray | int]: ...
def make(id: Literal["FrozenLake-v1", "FrozenLake8x8-v1"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, int]]: ...
@overload
def make(id: Literal["CliffWalking-v0"], **kwargs) -> Env[np.ndarray, np.ndarray | int]: ...
def make(id: Literal["CliffWalking-v0"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, int]]: ...
@overload
def make(id: Literal["Taxi-v3"], **kwargs) -> Env[np.ndarray, np.ndarray | int]: ...
def make(id: Literal["Taxi-v3"], **kwargs) -> Env[np.ndarray, Union[np.ndarray, int]]: ...
# Mujoco
# ----------------------------------------
@@ -415,7 +411,7 @@ class EnvRegistry(dict):
# Global registry of environments. Meant to be accessed through `register` and `make`
registry: dict[str, EnvSpec] = EnvRegistry()
registry: Dict[str, EnvSpec] = EnvRegistry()
current_namespace: Optional[str] = None
@@ -522,7 +518,7 @@ def register(id: str, **kwargs):
def make(
id: str | EnvSpec,
id: Union[str, EnvSpec],
max_episode_steps: Optional[int] = None,
autoreset: bool = False,
disable_env_checker: bool = False,

6
gym/envs/toy_text/frozen_lake.py
View File

@@ -1,9 +1,7 @@
from __future__ import annotations
from contextlib import closing
from io import StringIO
from os import path
from typing import Optional
from typing import List, Optional
import numpy as np
@@ -31,7 +29,7 @@ MAPS = {
}
def generate_random_map(size: int = 8, p: float = 0.8) -> list[str]:
def generate_random_map(size: int = 8, p: float = 0.8) -> List[str]:
"""Generates a random valid map (one that has a path from start to goal)
Args:

16
gym/spaces/box.py
View File

@@ -1,7 +1,5 @@
"""Implementation of a space that represents closed boxes in euclidean space."""
from __future__ import annotations
from typing import Optional, Sequence, SupportsFloat, Union
from typing import List, Optional, Sequence, SupportsFloat, Tuple, Type, Union
import numpy as np
@@ -52,8 +50,8 @@ class Box(Space[np.ndarray]):
low: Union[SupportsFloat, np.ndarray],
high: Union[SupportsFloat, np.ndarray],
shape: Optional[Sequence[int]] = None,
dtype: type = np.float32,
seed: Optional[int | seeding.RandomNumberGenerator] = None,
dtype: Type = np.float32,
seed: Optional[Union[int, seeding.RandomNumberGenerator]] = None,
):
r"""Constructor of :class:`Box`.
@@ -105,7 +103,7 @@ class Box(Space[np.ndarray]):
assert isinstance(high, np.ndarray)
assert high.shape == shape, "high.shape doesn't match provided shape"
self._shape: tuple[int, ...] = shape
self._shape: Tuple[int, ...] = shape
low_precision = get_precision(low.dtype)
high_precision = get_precision(high.dtype)
@@ -121,7 +119,7 @@ class Box(Space[np.ndarray]):
super().__init__(self.shape, self.dtype, seed)
@property
def shape(self) -> tuple[int, ...]:
def shape(self) -> Tuple[int, ...]:
"""Has stricter type than gym.Space - never None."""
return self._shape
@@ -210,7 +208,7 @@ class Box(Space[np.ndarray]):
"""Convert a batch of samples from this space to a JSONable data type."""
return np.array(sample_n).tolist()
def from_jsonable(self, sample_n: Sequence[SupportsFloat]) -> list[np.ndarray]:
def from_jsonable(self, sample_n: Sequence[SupportsFloat]) -> List[np.ndarray]:
"""Convert a JSONable data type to a batch of samples from this space."""
return [np.asarray(sample) for sample in sample_n]
@@ -278,7 +276,7 @@ def get_precision(dtype) -> SupportsFloat:
def _broadcast(
value: Union[SupportsFloat, np.ndarray],
dtype,
shape: tuple[int, ...],
shape: Tuple[int, ...],
inf_sign: str,
) -> np.ndarray:
"""Handle infinite bounds and broadcast at the same time if needed."""

14
gym/spaces/dict.py
View File

@@ -1,10 +1,8 @@
"""Implementation of a space that represents the cartesian product of other spaces as a dictionary."""
from __future__ import annotations
from collections import OrderedDict
from collections.abc import Mapping, Sequence
from typing import Dict as TypingDict
from typing import Optional
from typing import Optional, Union
import numpy as np
@@ -53,8 +51,8 @@ class Dict(Space[TypingDict[str, Space]], Mapping):
def __init__(
self,
spaces: Optional[dict[str, Space]] = None,
seed: Optional[dict | int | seeding.RandomNumberGenerator] = None,
spaces: Optional[TypingDict[str, Space]] = None,
seed: Optional[Union[dict, int, seeding.RandomNumberGenerator]] = None,
**spaces_kwargs: Space,
):
"""Constructor of :class:`Dict` space.
@@ -101,7 +99,7 @@ class Dict(Space[TypingDict[str, Space]], Mapping):
None, None, seed # type: ignore
) # None for shape and dtype, since it'll require special handling
def seed(self, seed: Optional[dict | int] = None) -> list:
def seed(self, seed: Optional[Union[dict, int]] = None) -> list:
"""Seed the PRNG of this space and all subspaces."""
seeds = []
if isinstance(seed, dict):
@@ -188,9 +186,9 @@ class Dict(Space[TypingDict[str, Space]], Mapping):
for key, space in self.spaces.items()
}
def from_jsonable(self, sample_n: dict[str, list]) -> list:
def from_jsonable(self, sample_n: TypingDict[str, list]) -> list:
"""Convert a JSONable data type to a batch of samples from this space."""
dict_of_list: dict[str, list] = {}
dict_of_list: TypingDict[str, list] = {}
for key, space in self.spaces.items():
dict_of_list[key] = space.from_jsonable(sample_n[key])
ret = []

6
gym/spaces/discrete.py
View File

@@ -1,7 +1,5 @@
"""Implementation of a space consisting of finitely many elements."""
from __future__ import annotations
from typing import Optional
from typing import Optional, Union
import numpy as np
@@ -23,7 +21,7 @@ class Discrete(Space[int]):
def __init__(
self,
n: int,
seed: Optional[int | seeding.RandomNumberGenerator] = None,
seed: Optional[Union[int, seeding.RandomNumberGenerator]] = None,
start: int = 0,
):
r"""Constructor of :class:`Discrete` space.

8
gym/spaces/multi_binary.py
View File

@@ -1,7 +1,5 @@
"""Implementation of a space that consists of binary np.ndarrays of a fixed shape."""
from __future__ import annotations
from typing import Optional, Sequence, Union
from typing import Optional, Sequence, Tuple, Union
import numpy as np
@@ -29,7 +27,7 @@ class MultiBinary(Space[np.ndarray]):
def __init__(
self,
n: Union[np.ndarray, Sequence[int], int],
seed: Optional[int | seeding.RandomNumberGenerator] = None,
seed: Optional[Union[int, seeding.RandomNumberGenerator]] = None,
):
"""Constructor of :class:`MultiBinary` space.
@@ -49,7 +47,7 @@ class MultiBinary(Space[np.ndarray]):
super().__init__(input_n, np.int8, seed)
@property
def shape(self) -> tuple[int, ...]:
def shape(self) -> Tuple[int, ...]:
"""Has stricter type than gym.Space - never None."""
return self._shape # type: ignore

10
gym/spaces/multi_discrete.py
View File

@@ -1,7 +1,5 @@
"""Implementation of a space that represents the cartesian product of `Discrete` spaces."""
from __future__ import annotations
from typing import Iterable, Optional, Sequence, Union
from typing import Iterable, List, Optional, Sequence, Tuple, Union
import numpy as np
@@ -31,9 +29,9 @@ class MultiDiscrete(Space[np.ndarray]):
def __init__(
self,
nvec: Union[np.ndarray, list[int]],
nvec: Union[np.ndarray, List[int]],
dtype=np.int64,
seed: Optional[int | seeding.RandomNumberGenerator] = None,
seed: Optional[Union[int, seeding.RandomNumberGenerator]] = None,
):
"""Constructor of :class:`MultiDiscrete` space.
@@ -61,7 +59,7 @@ class MultiDiscrete(Space[np.ndarray]):
super().__init__(self.nvec.shape, dtype, seed)
@property
def shape(self) -> tuple[int, ...]:
def shape(self) -> Tuple[int, ...]:
"""Has stricter type than :class:`gym.Space` - never None."""
return self._shape # type: ignore

24
gym/spaces/space.py
View File

@@ -1,7 +1,17 @@
"""Implementation of the `Space` metaclass."""
from __future__ import annotations
from typing import Generic, Iterable, Mapping, Optional, Sequence, TypeVar
from typing import (
Generic,
Iterable,
List,
Mapping,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
)
import numpy as np
@@ -39,8 +49,8 @@ class Space(Generic[T_cov]):
def __init__(
self,
shape: Optional[Sequence[int]] = None,
dtype: Optional[type | str | np.dtype] = None,
seed: Optional[int | seeding.RandomNumberGenerator] = None,
dtype: Optional[Union[Type, str, np.dtype]] = None,
seed: Optional[Union[int, seeding.RandomNumberGenerator]] = None,
):
"""Constructor of :class:`Space`.
@@ -67,7 +77,7 @@ class Space(Generic[T_cov]):
return self._np_random # type: ignore ## self.seed() call guarantees right type.
@property
def shape(self) -> Optional[tuple[int, ...]]:
def shape(self) -> Optional[Tuple[int, ...]]:
"""Return the shape of the space as an immutable property."""
return self._shape
@@ -88,7 +98,7 @@ class Space(Generic[T_cov]):
"""Return boolean specifying if x is a valid member of this space."""
return self.contains(x)
def __setstate__(self, state: Iterable | Mapping):
def __setstate__(self, state: Union[Iterable, Mapping]):
"""Used when loading a pickled space.
This method was implemented explicitly to allow for loading of legacy states.
@@ -119,7 +129,7 @@ class Space(Generic[T_cov]):
# By default, assume identity is JSONable
return list(sample_n)
def from_jsonable(self, sample_n: list) -> list[T_cov]:
def from_jsonable(self, sample_n: list) -> List[T_cov]:
"""Convert a JSONable data type to a batch of samples from this space."""
# By default, assume identity is JSONable
return sample_n

8
gym/spaces/tuple.py
View File

@@ -1,7 +1,5 @@
"""Implementation of a space that represents the cartesian product of other spaces."""
from __future__ import annotations
from typing import Iterable, Optional, Sequence
from typing import Iterable, List, Optional, Sequence, Union
import numpy as np
@@ -25,7 +23,7 @@ class Tuple(Space[tuple], Sequence):
def __init__(
self,
spaces: Iterable[Space],
seed: Optional[int | list[int] | seeding.RandomNumberGenerator] = None,
seed: Optional[Union[int, List[int], seeding.RandomNumberGenerator]] = None,
):
r"""Constructor of :class:`Tuple` space.
@@ -43,7 +41,7 @@ class Tuple(Space[tuple], Sequence):
), "Elements of the tuple must be instances of gym.Space"
super().__init__(None, None, seed) # type: ignore
def seed(self, seed: Optional[int | list[int]] = None) -> list:
def seed(self, seed: Optional[Union[int, List[int]]] = None) -> list:
"""Seed the PRNG of this space and all subspaces."""
seeds = []

6
gym/spaces/utils.py
View File

@@ -3,8 +3,6 @@
These functions mostly take care of flattening and unflattening elements of spaces
to facilitate their usage in learning code.
"""
from __future__ import annotations
import operator as op
from collections import OrderedDict
from functools import reduce, singledispatch
@@ -142,7 +140,9 @@ def unflatten(space: Space[T], x: np.ndarray) -> T:
@unflatten.register(Box)
@unflatten.register(MultiBinary)
def _unflatten_box_multibinary(space: Box | MultiBinary, x: np.ndarray) -> np.ndarray:
def _unflatten_box_multibinary(
space: Union[Box, MultiBinary], x: np.ndarray
) -> np.ndarray:
return np.asarray(x, dtype=space.dtype).reshape(space.shape)

14
gym/utils/play.py
View File

@@ -1,8 +1,6 @@
"""Utilities of visualising an environment."""
from __future__ import annotations
from collections import deque
from typing import Callable, Dict, Optional, Tuple, Union
from typing import Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import pygame
@@ -35,7 +33,7 @@ class PlayableGame:
def __init__(
self,
env: Env,
keys_to_action: Optional[dict[tuple[int], int]] = None,
keys_to_action: Optional[Dict[Tuple[int], int]] = None,
zoom: Optional[float] = None,
):
"""Wraps an environment with a dictionary of keyboard buttons to action and if to zoom in on the environment.
@@ -53,7 +51,7 @@ class PlayableGame:
self.running = True
def _get_relevant_keys(
self, keys_to_action: Optional[dict[tuple[int], int]] = None
self, keys_to_action: Optional[Dict[Tuple[int], int]] = None
) -> set:
if keys_to_action is None:
if hasattr(self.env, "get_keys_to_action"):
@@ -68,7 +66,7 @@ class PlayableGame:
relevant_keys = set(sum((list(k) for k in keys_to_action.keys()), []))
return relevant_keys
def _get_video_size(self, zoom: Optional[float] = None) -> tuple[int, int]:
def _get_video_size(self, zoom: Optional[float] = None) -> Tuple[int, int]:
# TODO: this needs to be updated when the render API change goes through
rendered = self.env.render(mode="rgb_array")
video_size = [rendered.shape[1], rendered.shape[0]]
@@ -103,7 +101,7 @@ class PlayableGame:
def display_arr(
screen: Surface, arr: np.ndarray, video_size: tuple[int, int], transpose: bool
screen: Surface, arr: np.ndarray, video_size: Tuple[int, int], transpose: bool
):
"""Displays a numpy array on screen.
@@ -273,7 +271,7 @@ class PlayPlot:
"""
def __init__(
self, callback: callable, horizon_timesteps: int, plot_names: list[str]
self, callback: callable, horizon_timesteps: int, plot_names: List[str]
):
"""Constructor of :class:`PlayPlot`.

10
gym/utils/seeding.py
View File

@@ -1,10 +1,8 @@
"""Set of random number generator functions: seeding, generator, hashing seeds."""
from __future__ import annotations
import hashlib
import os
import struct
from typing import Any, Optional, Union
from typing import Any, List, Optional, Tuple, Union
import numpy as np
@@ -12,7 +10,7 @@ from gym import error
from gym.logger import deprecation
def np_random(seed: Optional[int] = None) -> tuple[RandomNumberGenerator, Any]:
def np_random(seed: Optional[int] = None) -> Tuple["RandomNumberGenerator", Any]:
"""Generates a random number generator from the seed and returns the Generator and seed.
Args:
@@ -216,7 +214,7 @@ def _bigint_from_bytes(bt: bytes) -> int:
return accum
def _int_list_from_bigint(bigint: int) -> list[int]:
def _int_list_from_bigint(bigint: int) -> List[int]:
deprecation(
"Function `_int_list_from_bigint` is marked as deprecated and will be removed in the future. "
)
@@ -226,7 +224,7 @@ def _int_list_from_bigint(bigint: int) -> list[int]:
elif bigint == 0:
return [0]
ints: list[int] = []
ints: List[int] = []
while bigint > 0:
bigint, mod = divmod(bigint, 2**32)
ints.append(mod)

6
gym/vector/__init__.py
View File

@@ -1,7 +1,5 @@
"""Module for vector environments."""
from __future__ import annotations
from typing import Iterable, Optional, Union
from typing import Iterable, List, Optional, Union
from gym.vector.async_vector_env import AsyncVectorEnv
from gym.vector.sync_vector_env import SyncVectorEnv
@@ -14,7 +12,7 @@ def make(
id: str,
num_envs: int = 1,
asynchronous: bool = True,
wrappers: Optional[Union[callable, list[callable]]] = None,
wrappers: Optional[Union[callable, List[callable]]] = None,
**kwargs,
) -> VectorEnv:
"""Create a vectorized environment from multiple copies of an environment, from its id.

10
gym/vector/async_vector_env.py
View File

@@ -1,12 +1,10 @@
"""An async vector environment."""
from __future__ import annotations
import multiprocessing as mp
import sys
import time
from copy import deepcopy
from enum import Enum
from typing import Optional, Sequence, Union
from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
@@ -199,7 +197,7 @@ class AsyncVectorEnv(VectorEnv):
def reset_async(
self,
seed: Optional[Union[int, list[int]]] = None,
seed: Optional[Union[int, List[int]]] = None,
return_info: bool = False,
options: Optional[dict] = None,
):
@@ -250,7 +248,7 @@ class AsyncVectorEnv(VectorEnv):
seed: Optional[int] = None,
return_info: bool = False,
options: Optional[dict] = None,
) -> Union[ObsType, tuple[ObsType, list[dict]]]:
) -> Union[ObsType, Tuple[ObsType, List[dict]]]:
"""Waits for the calls triggered by :meth:`reset_async` to finish and returns the results.
Args:
@@ -333,7 +331,7 @@ class AsyncVectorEnv(VectorEnv):
def step_wait(
self, timeout: Optional[Union[int, float]] = None
) -> tuple[np.ndarray, np.ndarray, np.ndarray, list[dict]]:
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, List[dict]]:
"""Wait for the calls to :obj:`step` in each sub-environment to finish.
Args:

6
gym/vector/sync_vector_env.py
View File

@@ -1,8 +1,6 @@
"""A synchronous vector environment."""
from __future__ import annotations
from copy import deepcopy
from typing import Any, Iterator, Optional, Sequence, Union
from typing import Any, Iterator, List, Optional, Sequence, Union
import numpy as np
@@ -89,7 +87,7 @@ class SyncVectorEnv(VectorEnv):
def reset_wait(
self,
seed: Optional[Union[int, list[int]]] = None,
seed: Optional[Union[int, List[int]]] = None,
return_info: bool = False,
options: Optional[dict] = None,
):

14
gym/vector/vector_env.py
View File

@@ -1,7 +1,5 @@
"""Base class for vectorized environments."""
from __future__ import annotations
from typing import Any, Optional, Union
from typing import Any, List, Optional, Union
import numpy as np
@@ -50,7 +48,7 @@ class VectorEnv(gym.Env):
def reset_async(
self,
seed: Optional[Union[int, list[int]]] = None,
seed: Optional[Union[int, List[int]]] = None,
return_info: bool = False,
options: Optional[dict] = None,
):
@@ -68,7 +66,7 @@ class VectorEnv(gym.Env):
def reset_wait(
self,
seed: Optional[Union[int, list[int]]] = None,
seed: Optional[Union[int, List[int]]] = None,
return_info: bool = False,
options: Optional[dict] = None,
):
@@ -91,7 +89,7 @@ class VectorEnv(gym.Env):
def reset(
self,
*,
seed: Optional[Union[int, list[int]]] = None,
seed: Optional[Union[int, List[int]]] = None,
return_info: bool = False,
options: Optional[dict] = None,
):
@@ -144,10 +142,10 @@ class VectorEnv(gym.Env):
def call_async(self, name, *args, **kwargs):
"""Calls a method name for each parallel environment asynchronously."""
def call_wait(self, **kwargs) -> list[Any]:
def call_wait(self, **kwargs) -> List[Any]:
"""After calling a method in :meth:`call_async`, this function collects the results."""
def call(self, name: str, *args, **kwargs) -> list[Any]:
def call(self, name: str, *args, **kwargs) -> List[Any]:
"""Call a method, or get a property, from each parallel environment.
Args:

6
gym/wrappers/monitoring/video_recorder.py
View File

@@ -1,6 +1,4 @@
"""A wrapper for video recording environments by rolling it out, frame by frame."""
from __future__ import annotations
import json
import os
import os.path
@@ -9,7 +7,7 @@ import shutil
import subprocess
import tempfile
from io import StringIO
from typing import Optional, Union
from typing import Optional, Tuple, Union
import numpy as np
@@ -362,7 +360,7 @@ class ImageEncoder:
def __init__(
self,
output_path: str,
frame_shape: tuple[int, int, int],
frame_shape: Tuple[int, int, int],
frames_per_sec: int,
output_frames_per_sec: int,
):

8
gym/wrappers/pixel_observation.py
View File

@@ -1,10 +1,8 @@
"""Wrapper for augmenting observations by pixel values."""
from __future__ import annotations
import collections
import copy
from collections.abc import MutableMapping
from typing import Any, Optional
from typing import Any, Dict, Optional, Tuple
import numpy as np
@@ -52,8 +50,8 @@ class PixelObservationWrapper(gym.ObservationWrapper):
self,
env: gym.Env,
pixels_only: bool = True,
render_kwargs: Optional[dict[str, dict[str, Any]]] = None,
pixel_keys: tuple[str, ...] = ("pixels",),
render_kwargs: Optional[Dict[str, Dict[str, Any]]] = None,
pixel_keys: Tuple[str, ...] = ("pixels",),
):
"""Initializes a new pixel Wrapper.

3
py.Dockerfile
View File

@@ -14,6 +14,7 @@ ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/root/.mujoco/mujoco210/bin
COPY . /usr/local/gym/
WORKDIR /usr/local/gym/
RUN pip install .[noatari] && pip install -r test_requirements.txt
RUN if [ python:$PYTHON_VERSION = "python:3.6.15" ] ; then pip install .[box2d,classic_control,toy_text,other] ; else pip install .[noatari] ; fi
RUN pip install -r test_requirements.txt
ENTRYPOINT ["/usr/local/gym/bin/docker_entrypoint"]

2
pyproject.toml
View File

@@ -25,7 +25,7 @@ strict = [
]
typeCheckingMode = "basic"
pythonVersion = "3.7"
pythonVersion = "3.6"
typeshedPath = "typeshed"
enableTypeIgnoreComments = true

6
setup.py
View File

@@ -55,8 +55,9 @@ setup(
install_requires=[
"numpy>=1.18.0",
"cloudpickle>=1.2.0",
"importlib_metadata>=4.10.0; python_version < '3.10'",
"importlib_metadata>=4.8.0; python_version < '3.10'",
"gym_notices>=0.0.4",
"dataclasses==0.8; python_version == '3.6'",
],
extras_require=extras,
package_data={
@@ -69,9 +70,10 @@ setup(
]
},
tests_require=["pytest", "mock"],
python_requires=">=3.7",
python_requires=">=3.6",
classifiers=[
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",

1
test_requirements.txt
View File

@@ -1,3 +1,2 @@
lz4~=3.1
pytest~=6.2
pytest-forked~=1.3

13
tests/envs/spec_list.py
View File

@@ -1,7 +1,10 @@
from gym import envs, logger
SKIP_MUJOCO_V3_WARNING_MESSAGE = (
"Cannot run mujoco test because mujoco-py is not installed"
"Cannot run mujoco test because `mujoco-py` is not installed"
)
SKIP_MUJOCO_V4_WARNING_MESSAGE = (
"Cannot run mujoco test because `mujoco` is not installed"
)
skip_mujoco_v3 = False
@@ -10,13 +13,19 @@ try:
except ImportError:
skip_mujoco_v3 = True
skip_mujoco_v4 = False
try:
import mujoco # noqa:F401
except ImportError:
skip_mujoco_v4 = True
def should_skip_env_spec_for_tests(spec):
# We skip tests for envs that require dependencies or are otherwise
# troublesome to run frequently
ep = spec.entry_point
# Skip mujoco tests for pull request CI
if skip_mujoco_v3 and ep.startswith("gym.envs.mujoco"):
if (skip_mujoco_v3 or skip_mujoco_v4) and ep.startswith("gym.envs.mujoco"):
return True
try:
import gym.envs.atari # noqa:F401

27
tests/envs/test_action_dim_check.py
View File

@@ -3,11 +3,11 @@ from typing import List
import numpy as np
import pytest
from gym import envs
import gym
from gym import Env
from gym.envs.registration import EnvSpec
from gym.spaces.box import Box
from gym.spaces.discrete import Discrete
from gym.spaces.space import Space
from tests.envs.spec_list import (
SKIP_MUJOCO_V3_WARNING_MESSAGE,
skip_mujoco_v3,
@@ -17,17 +17,20 @@ from tests.envs.spec_list import (
ENVIRONMENT_IDS = ("HalfCheetah-v2",)
def make_envs_by_action_space_type(spec_list: List[EnvSpec], action_space: Space):
def filters_envs_action_space_type(
env_spec_list: List[EnvSpec], action_space: type
) -> List[Env]:
"""Make environments of specific action_space type.
This function returns a filtered list of environment from the
spec_list that matches the action_space type.
This function returns a filtered list of environment from the spec_list that matches the action_space type.
Args:
spec_list (list): list of registered environments' specification
env_spec_list (list): list of registered environments' specification
action_space (gym.spaces.Space): action_space type
"""
filtered_envs = []
for spec in spec_list:
env = envs.make(spec.id)
for spec in env_spec_list:
env = gym.make(spec.id)
if isinstance(env.action_space, action_space):
filtered_envs.append(env)
return filtered_envs
@@ -36,7 +39,7 @@ def make_envs_by_action_space_type(spec_list: List[EnvSpec], action_space: Space
@pytest.mark.skipif(skip_mujoco_v3, reason=SKIP_MUJOCO_V3_WARNING_MESSAGE)
@pytest.mark.parametrize("environment_id", ENVIRONMENT_IDS)
def test_serialize_deserialize(environment_id):
env = envs.make(environment_id)
env = gym.make(environment_id)
env.reset()
with pytest.raises(ValueError, match="Action dimension mismatch"):
@@ -46,7 +49,7 @@ def test_serialize_deserialize(environment_id):
env.step(0.1)
@pytest.mark.parametrize("env", make_envs_by_action_space_type(spec_list, Discrete))
@pytest.mark.parametrize("env", filters_envs_action_space_type(spec_list, Discrete))
def test_discrete_actions_out_of_bound(env):
"""Test out of bound actions in Discrete action_space.
In discrete action_space environments, `out-of-bound`
@@ -65,7 +68,7 @@ def test_discrete_actions_out_of_bound(env):
@pytest.mark.parametrize(
("env", "seed"),
[(env, 42) for env in make_envs_by_action_space_type(spec_list, Box)],
[(env, 42) for env in filters_envs_action_space_type(spec_list, Box)],
)
def test_box_actions_out_of_bound(env, seed):
"""Test out of bound actions in Box action_space.
@@ -80,7 +83,7 @@ def test_box_actions_out_of_bound(env, seed):
env.reset(seed=seed)
oob_env = envs.make(env.spec.id)
oob_env = gym.make(env.spec.id)
oob_env.reset(seed=seed)
dtype = env.action_space.dtype