mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-01 22:11:25 +00:00
4c460ba6c8
* add dtype to Box
* remove board_game, debugging, safety, parameter_tuning environments
* massive set of breaking changes
- remove python logging module
- _step, _reset, _seed, _close => non underscored method
- remove benchmark and scoring folder
* Improve render("human"), now resizable, closable window.
* get rid of default step and reset in wrappers, so it doesn’t silently fail for people with underscore methods
* CubeCrash unit test environment
* followup fixes
* MemorizeDigits unit test envrionment
* refactored spaces a bit
fixed indentation
disabled test_env_semantics
* fix unit tests
* fixes
* CubeCrash, MemorizeDigits tested
* gym backwards compatibility patch
* gym backwards compatibility, followup fixes
* changelist, add spaces to main namespaces
* undo_logger_setup for backwards compat
* remove configuration.py
73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
from gym import Space
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from collections import OrderedDict
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class Dict(Space):
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"""
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A dictionary of simpler spaces.
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Example usage:
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self.observation_space = spaces.Dict({"position": spaces.Discrete(2), "velocity": spaces.Discrete(3)})
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Example usage [nested]:
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self.nested_observation_space = spaces.Dict({
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'sensors': spaces.Dict({
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'position': spaces.Box(low=-100, high=100, shape=(3)),
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'velocity': spaces.Box(low=-1, high=1, shape=(3)),
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'front_cam': spaces.Tuple((
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spaces.Box(low=0, high=1, shape=(10, 10, 3)),
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spaces.Box(low=0, high=1, shape=(10, 10, 3))
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)),
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'rear_cam': spaces.Box(low=0, high=1, shape=(10, 10, 3)),
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}),
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'ext_controller': spaces.MultiDiscrete([ [0,4], [0,1], [0,1] ]),
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'inner_state':spaces.Dict({
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'charge': spaces.Discrete(100),
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'system_checks': spaces.MultiBinary(10),
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'job_status': spaces.Dict({
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'task': spaces.Discrete(5),
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'progress': spaces.Box(low=0, high=100, shape=()),
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})
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})
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})
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"""
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def __init__(self, spaces):
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if isinstance(spaces, dict):
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spaces = OrderedDict(sorted(list(spaces.items())))
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if isinstance(spaces, list):
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spaces = OrderedDict(spaces)
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self.spaces = spaces
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Space.__init__(self, None, None) # None for shape and dtype, since it'll require special handling
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def sample(self):
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return OrderedDict([(k, space.sample()) for k, space in self.spaces.items()])
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def contains(self, x):
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if not isinstance(x, dict) or len(x) != len(self.spaces):
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return False
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for k, space in self.spaces.items():
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if k not in x:
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return False
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if not space.contains(x[k]):
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return False
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return True
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def __repr__(self):
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return "Dict(" + ", ". join([k + ":" + str(s) for k, s in self.spaces.items()]) + ")"
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def to_jsonable(self, sample_n):
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# serialize as dict-repr of vectors
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return {key: space.to_jsonable([sample[key] for sample in sample_n]) \
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for key, space in self.spaces.items()}
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def from_jsonable(self, sample_n):
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dict_of_list = {}
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for key, space in self.spaces.items():
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dict_of_list[key] = space.from_jsonable(sample_n[key])
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ret = []
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for i, _ in enumerate(dict_of_list[key]):
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entry = {}
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for key, value in dict_of_list.items():
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entry[key] = value[i]
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ret.append(entry)
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return ret
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