mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-07-31 22:04:31 +00:00
e913bc81b8
* 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
83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
from __future__ import annotations
|
|
|
|
from collections.abc import Sequence
|
|
|
|
import numpy as np
|
|
|
|
from gym import logger
|
|
|
|
from .discrete import Discrete
|
|
from .space import Space
|
|
|
|
|
|
class MultiDiscrete(Space[np.ndarray]):
|
|
"""
|
|
- 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
|
|
|
|
Note: Some environment wrappers assume a value of 0 always represents the NOOP action.
|
|
|
|
e.g. Nintendo Game Controller
|
|
- Can be conceptualized as 3 discrete action spaces:
|
|
|
|
1) Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4
|
|
2) Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
|
|
3) Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
|
|
|
|
- Can be initialized as
|
|
|
|
MultiDiscrete([ 5, 2, 2 ])
|
|
|
|
"""
|
|
|
|
def __init__(self, nvec: list[int], dtype=np.int64, seed=None):
|
|
"""
|
|
nvec: vector of counts of each categorical variable
|
|
"""
|
|
self.nvec = np.array(nvec, dtype=dtype, copy=True)
|
|
assert (self.nvec > 0).all(), "nvec (counts) have to be positive"
|
|
|
|
super().__init__(self.nvec.shape, dtype, seed)
|
|
|
|
@property
|
|
def shape(self) -> tuple[int, ...]:
|
|
"""Has stricter type than gym.Space - never None."""
|
|
return self._shape # type: ignore
|
|
|
|
def sample(self) -> np.ndarray:
|
|
return (self.np_random.random(self.nvec.shape) * self.nvec).astype(self.dtype)
|
|
|
|
def contains(self, x) -> bool:
|
|
if isinstance(x, Sequence):
|
|
x = np.array(x) # Promote list to array for contains check
|
|
# if nvec is uint32 and space dtype is uint32, then 0 <= x < self.nvec guarantees that x
|
|
# is within correct bounds for space dtype (even though x does not have to be unsigned)
|
|
return bool(x.shape == self.shape and (0 <= x).all() and (x < self.nvec).all())
|
|
|
|
def to_jsonable(self, sample_n):
|
|
return [sample.tolist() for sample in sample_n]
|
|
|
|
def from_jsonable(self, sample_n):
|
|
return np.array(sample_n)
|
|
|
|
def __repr__(self):
|
|
return f"MultiDiscrete({self.nvec})"
|
|
|
|
def __getitem__(self, index):
|
|
nvec = self.nvec[index]
|
|
if nvec.ndim == 0:
|
|
subspace = Discrete(nvec)
|
|
else:
|
|
subspace = MultiDiscrete(nvec, self.dtype)
|
|
subspace.np_random.bit_generator.state = self.np_random.bit_generator.state
|
|
return subspace
|
|
|
|
def __len__(self):
|
|
if self.nvec.ndim >= 2:
|
|
logger.warn("Get length of a multi-dimensional MultiDiscrete space.")
|
|
return len(self.nvec)
|
|
|
|
def __eq__(self, other):
|
|
return isinstance(other, MultiDiscrete) and np.all(self.nvec == other.nvec)
|