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https://github.com/Farama-Foundation/Gymnasium.git
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227e246ff6
* Moved pygame imports into render * Formatting * Make pygame optional for box2d, try to make formatting work * fix tests, fix pre-commit. * Update ci linter config. * fix type hints for latest pyright version and backward compatibility with numpy <= 1.21.5 * pre-commit. Co-authored-by: Ariel Kwiatkowski <ariel.j.kwiatkowski@gmail.com> Co-authored-by: Gianluca De Cola <gianluca.decola@ags-it.com>
81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
from __future__ import annotations
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from collections.abc import Sequence
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from typing import Iterable
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import numpy as np
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from gym import logger
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from .discrete import Discrete
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from .space import Space
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class MultiDiscrete(Space[np.ndarray]):
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"""
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The multi-discrete action space consists of a series of discrete action spaces with different number of actions in each. It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space. It is parametrized by passing an array of positive integers specifying number of actions for each discrete action space.
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Note:
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Some environment wrappers assume a value of 0 always represents the NOOP action.
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e.g. Nintendo Game Controller - Can be conceptualized as 3 discrete action spaces:
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1. Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4
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2. Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
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3. Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
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It can be initialized as ``MultiDiscrete([ 5, 2, 2 ])``
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"""
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def __init__(self, nvec: list[int], dtype=np.int64, seed=None):
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"""
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nvec: vector of counts of each categorical variable
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"""
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self.nvec = np.array(nvec, dtype=dtype, copy=True)
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assert (self.nvec > 0).all(), "nvec (counts) have to be positive"
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super().__init__(self.nvec.shape, dtype, seed)
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@property
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def shape(self) -> tuple[int, ...]:
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"""Has stricter type than gym.Space - never None."""
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return self._shape # type: ignore
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def sample(self) -> np.ndarray:
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return (self.np_random.random(self.nvec.shape) * self.nvec).astype(self.dtype)
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def contains(self, x) -> bool:
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if isinstance(x, Sequence):
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x = np.array(x) # Promote list to array for contains check
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# if nvec is uint32 and space dtype is uint32, then 0 <= x < self.nvec guarantees that x
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# is within correct bounds for space dtype (even though x does not have to be unsigned)
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return bool(x.shape == self.shape and (0 <= x).all() and (x < self.nvec).all())
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def to_jsonable(self, sample_n: Iterable[np.ndarray]):
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return [sample.tolist() for sample in sample_n]
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def from_jsonable(self, sample_n):
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return np.array(sample_n)
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def __repr__(self):
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return f"MultiDiscrete({self.nvec})"
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def __getitem__(self, index):
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nvec = self.nvec[index]
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if nvec.ndim == 0:
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subspace = Discrete(nvec)
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else:
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subspace = MultiDiscrete(nvec, self.dtype) # type: ignore
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subspace.np_random.bit_generator.state = self.np_random.bit_generator.state
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return subspace
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def __len__(self):
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if self.nvec.ndim >= 2:
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logger.warn("Get length of a multi-dimensional MultiDiscrete space.")
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return len(self.nvec)
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def __eq__(self, other):
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return isinstance(other, MultiDiscrete) and np.all(self.nvec == other.nvec)
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