mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-08 16:55:22 +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
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
import numpy as np
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from gym import Space, spaces, logger
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class Box(Space):
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"""
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A box in R^n.
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I.e., each coordinate is bounded.
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Example usage:
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self.action_space = spaces.Box(low=-10, high=10, shape=(1,))
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"""
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def __init__(self, low=None, high=None, shape=None, dtype=None):
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"""
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Two kinds of valid input:
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Box(low=-1.0, high=1.0, shape=(3,4)) # low and high are scalars, and shape is provided
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Box(np.array(low=[-1.0,-2.0]), high=np.array([2.0,4.0])) # low and high are arrays of the same shape
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"""
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if shape is None:
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assert low.shape == high.shape
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shape = low.shape
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else:
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assert np.isscalar(low) and np.isscalar(high)
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low = low + np.zeros(shape)
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high = high + np.zeros(shape)
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if dtype is None: # Autodetect type
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if (high == 255).all():
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dtype = np.uint8
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else:
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dtype = np.float32
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logger.warn("gym.spaces.Box autodetected dtype as %s. Please provide explicit dtype." % dtype)
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self.low = low.astype(dtype)
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self.high = high.astype(dtype)
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Space.__init__(self, shape, dtype)
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def sample(self):
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return spaces.np_random.uniform(low=self.low, high=self.high + (0 if self.dtype.kind == 'f' else 1), size=self.low.shape).astype(self.dtype)
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def contains(self, x):
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return x.shape == self.shape and (x >= self.low).all() and (x <= self.high).all()
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def to_jsonable(self, sample_n):
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return np.array(sample_n).tolist()
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def from_jsonable(self, sample_n):
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return [np.asarray(sample) for sample in sample_n]
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def __repr__(self):
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return "Box" + str(self.shape)
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def __eq__(self, other):
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return np.allclose(self.low, other.low) and np.allclose(self.high, other.high)
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