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* feat: add `isort` to `pre-commit` * ci: skip `__init__.py` file for `isort` * ci: make `isort` mandatory in lint pipeline * docs: add a section on Git hooks * ci: check isort diff * fix: isort from master branch * docs: add pre-commit badge * ci: update black + bandit versions * feat: add PR template * refactor: PR template * ci: remove bandit * docs: add Black badge * ci: try to remove all `|| true` statements * ci: remove lint_python job - Remove `lint_python` CI job - Move `pyupgrade` job to `pre-commit` workflow * fix: avoid messing with typing * docs: add a note on running `pre-cpmmit` manually * ci: apply `pre-commit` to the whole codebase
145 lines
4.3 KiB
Python
145 lines
4.3 KiB
Python
from collections import OrderedDict
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from functools import singledispatch
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import numpy as np
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from gym.spaces import Box, Dict, Discrete, MultiBinary, MultiDiscrete, Space, Tuple
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from gym.vector.utils.spaces import _BaseGymSpaces
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__all__ = ["concatenate", "create_empty_array"]
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@singledispatch
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def concatenate(space, items, out):
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"""Concatenate multiple samples from space into a single object.
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Parameters
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----------
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items : iterable of samples of `space`
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Samples to be concatenated.
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out : tuple, dict, or `np.ndarray`
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The output object. This object is a (possibly nested) numpy array.
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space : `gym.spaces.Space` instance
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Observation space of a single environment in the vectorized environment.
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Returns
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-------
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out : tuple, dict, or `np.ndarray`
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The output object. This object is a (possibly nested) numpy array.
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Example
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-------
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>>> from gym.spaces import Box
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>>> space = Box(low=0, high=1, shape=(3,), dtype=np.float32)
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>>> out = np.zeros((2, 3), dtype=np.float32)
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>>> items = [space.sample() for _ in range(2)]
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>>> concatenate(items, out, space)
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array([[0.6348213 , 0.28607962, 0.60760117],
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[0.87383074, 0.192658 , 0.2148103 ]], dtype=float32)
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"""
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assert isinstance(items, (list, tuple))
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raise ValueError(
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f"Space of type `{type(space)}` is not a valid `gym.Space` instance."
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)
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@concatenate.register(Box)
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@concatenate.register(Discrete)
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@concatenate.register(MultiDiscrete)
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@concatenate.register(MultiBinary)
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def _concatenate_base(space, items, out):
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return np.stack(items, axis=0, out=out)
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@concatenate.register(Tuple)
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def _concatenate_tuple(space, items, out):
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return tuple(
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concatenate(subspace, [item[i] for item in items], out[i])
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for (i, subspace) in enumerate(space.spaces)
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)
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@concatenate.register(Dict)
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def _concatenate_dict(space, items, out):
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return OrderedDict(
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[
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(key, concatenate(subspace, [item[key] for item in items], out[key]))
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for (key, subspace) in space.spaces.items()
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]
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)
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@concatenate.register(Space)
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def _concatenate_custom(space, items, out):
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return tuple(items)
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@singledispatch
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def create_empty_array(space, n=1, fn=np.zeros):
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"""Create an empty (possibly nested) numpy array.
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Parameters
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----------
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space : `gym.spaces.Space` instance
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Observation space of a single environment in the vectorized environment.
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n : int
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Number of environments in the vectorized environment. If `None`, creates
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an empty sample from `space`.
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fn : callable
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Function to apply when creating the empty numpy array. Examples of such
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functions are `np.empty` or `np.zeros`.
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Returns
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-------
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out : tuple, dict, or `np.ndarray`
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The output object. This object is a (possibly nested) numpy array.
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Example
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-------
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>>> from gym.spaces import Box, Dict
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>>> space = Dict({
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... 'position': Box(low=0, high=1, shape=(3,), dtype=np.float32),
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... 'velocity': Box(low=0, high=1, shape=(2,), dtype=np.float32)})
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>>> create_empty_array(space, n=2, fn=np.zeros)
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OrderedDict([('position', array([[0., 0., 0.],
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[0., 0., 0.]], dtype=float32)),
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('velocity', array([[0., 0.],
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[0., 0.]], dtype=float32))])
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"""
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raise ValueError(
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f"Space of type `{type(space)}` is not a valid `gym.Space` instance."
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)
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@create_empty_array.register(Box)
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@create_empty_array.register(Discrete)
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@create_empty_array.register(MultiDiscrete)
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@create_empty_array.register(MultiBinary)
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def _create_empty_array_base(space, n=1, fn=np.zeros):
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shape = space.shape if (n is None) else (n,) + space.shape
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return fn(shape, dtype=space.dtype)
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@create_empty_array.register(Tuple)
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def _create_empty_array_tuple(space, n=1, fn=np.zeros):
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return tuple(create_empty_array(subspace, n=n, fn=fn) for subspace in space.spaces)
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@create_empty_array.register(Dict)
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def _create_empty_array_dict(space, n=1, fn=np.zeros):
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return OrderedDict(
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[
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(key, create_empty_array(subspace, n=n, fn=fn))
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for (key, subspace) in space.spaces.items()
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]
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)
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@create_empty_array.register(Space)
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def _create_empty_array_custom(space, n=1, fn=np.zeros):
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return None
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