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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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16
gym/spaces/box.py
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@@ -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."""