gaspersic/Gymnasium
1
0
Fork 0
mirror of https://github.com/Farama-Foundation/Gymnasium.git synced 2025-09-17 23:10:05 +00:00
Code Issues Packages Projects Releases Wiki Activity

Added Action masking for Space.sample() (#2906)

Browse Source 
* Allows a new RNG to be generated with seed=-1 and updated env_checker to fix bug if environment doesn't use np_random in reset

* Revert "fixed `gym.vector.make` where the checker was being applied in the opposite case than was intended to (#2871)"

This reverts commit 519dfd9117.

* Remove bad pushed commits

* Fixed spelling in core.py

* Pins pytest to the last py 3.6 version

* Add support for action masking in Space.sample(mask=...)

* Fix action mask

* Fix action_mask

* Fix action_mask

* Added docstrings, fixed bugs and added taxi examples

* Fixed bugs

* Add tests for sample

* Add docstrings and test space sample mask Discrete and MultiBinary

* Add MultiDiscrete sampling and tests

* Remove sample mask from graph

* Update gym/spaces/multi_discrete.py

Co-authored-by: Markus Krimmel <montcyril@gmail.com>

* Updates based on Marcus28 and jjshoots for Graph.py

* Updates based on Marcus28 and jjshoots for Graph.py

* jjshoot review

* jjshoot review

* Update assert check

* Update type hints

Co-authored-by: Markus Krimmel <montcyril@gmail.com>
This commit is contained in:
Mark Towers
2022-06-26 23:23:15 +01:00
committed by GitHub
parent d750eb8df0
commit 024b0f5160
11 changed files with 562 additions and 71 deletions
Download Patch File Download Diff File Expand all files Collapse all files

75
gym/spaces/multi_discrete.py
View File

@@ -8,6 +8,8 @@ from gym.spaces.discrete import Discrete
from gym.spaces.space import Space
from gym.utils import seeding
SAMPLE_MASK_TYPE = Tuple[Union["SAMPLE_MASK_TYPE", np.ndarray], ...]
class MultiDiscrete(Space[np.ndarray]):
"""This represents the cartesian product of arbitrary :class:`Discrete` spaces.
@@ -23,8 +25,17 @@ class MultiDiscrete(Space[np.ndarray]):
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
It can be initialized as ``MultiDiscrete([ 5, 2, 2 ])``
It can be initialized as ``MultiDiscrete([ 5, 2, 2 ])`` such that a sample might be ``array([3, 1, 0])``.
Although this feature is rarely used, :class:`MultiDiscrete` spaces may also have several axes
if ``nvec`` has several axes:
Example::
>> d = MultiDiscrete(np.array([[1, 2], [3, 4]]))
>> d.sample()
array([[0, 0],
[2, 3]])
"""
def __init__(
@@ -37,16 +48,6 @@ class MultiDiscrete(Space[np.ndarray]):
The argument ``nvec`` will determine the number of values each categorical variable can take.
Although this feature is rarely used, :class:`MultiDiscrete` spaces may also have several axes
if ``nvec`` has several axes:
Example::
>> d = MultiDiscrete(np.array([[1, 2], [3, 4]]))
>> d.sample()
array([[0, 0],
[2, 3]])
Args:
nvec: vector of counts of each categorical variable. This will usually be a list of integers. However,
you may also pass a more complicated numpy array if you'd like the space to have several axes.
@@ -63,8 +64,56 @@ class MultiDiscrete(Space[np.ndarray]):
"""Has stricter type than :class:`gym.Space` - never None."""
return self._shape # type: ignore
def sample(self) -> np.ndarray:
"""Generates a single random sample this space."""
def sample(self, mask: Optional[SAMPLE_MASK_TYPE] = None) -> np.ndarray:
"""Generates a single random sample this space.
Args:
mask: An optional mask for multi-discrete, expects tuples with a `np.ndarray` mask in the position of each
action with shape `(n,)` where `n` is the number of actions and `dtype=np.int8`.
Only mask values == 1 are possible to sample unless all mask values for an action are 0 then the default action 0 is sampled.
Returns:
An `np.ndarray` of shape `space.shape`
"""
if mask is not None:
def _apply_mask(
sub_mask: SAMPLE_MASK_TYPE, sub_nvec: np.ndarray
) -> Union[int, List[int]]:
if isinstance(sub_mask, np.ndarray):
assert np.issubdtype(
type(sub_nvec), np.integer
), f"Expects the mask to be for an action, actual for {sub_nvec}"
assert (
len(sub_mask) == sub_nvec
), f"Expects the mask length to be equal to the number of actions, mask length: {len(sub_mask)}, nvec length: {sub_nvec}"
assert (
sub_mask.dtype == np.int8
), f"Expects the mask dtype to be np.int8, actual dtype: {sub_mask.dtype}"
valid_action_mask = sub_mask == 1
assert np.all(
np.logical_or(sub_mask == 0, valid_action_mask)
), f"Expects all masks values to 0 or 1, actual values: {sub_mask}"
if np.any(valid_action_mask):
return self.np_random.choice(np.where(valid_action_mask)[0])
else:
return 0
else:
assert isinstance(
sub_mask, tuple
), f"Expects the mask to be a tuple or np.ndarray, actual type: {type(sub_mask)}"
assert len(sub_mask) == len(
sub_nvec
), f"Expects the mask length to be equal to the number of actions, mask length: {len(sub_mask)}, nvec length: {len(sub_nvec)}"
return [
_apply_mask(new_mask, new_nvec)
for new_mask, new_nvec in zip(sub_mask, sub_nvec)
]
return np.array(_apply_mask(mask, self.nvec), dtype=self.dtype)
return (self.np_random.random(self.nvec.shape) * self.nvec).astype(self.dtype)
def contains(self, x) -> bool: