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https://github.com/Farama-Foundation/Gymnasium.git
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c6a97e17ee
* Initial version of vectorized environments * Raise an exception in the main process if child process raises an exception * Add list of exposed functions in vector module * Use deepcopy instead of np.copy * Add documentation for vector utils * Add tests for copy in AsyncVectorEnv * Add example in documentation for batch_space * Add cloudpickle dependency in setup.py * Fix __del__ in VectorEnv * Check if all observation spaces are equal in AsyncVectorEnv * Check if all observation spaces are equal in SyncVectorEnv * Fix spaces non equality in SyncVectorEnv for Python 2 * Handle None parameter in create_empty_array * Fix check_observation_space with spaces equality * Raise an exception when operations are out of order in AsyncVectorEnv * Add version requirement for cloudpickle * Use a state instead of binary flags in AsyncVectorEnv * Use numpy.zeros when initializing observations in vectorized environments * Remove poll from public API in AsyncVectorEnv * Remove close_extras from VectorEnv * Add test between AsyncVectorEnv and SyncVectorEnv * Remove close in check_observation_space * Add documentation for seed and close * Refactor exceptions for AsyncVectorEnv * Close pipes if the environment raises an error * Add tests for out of order operations * Change default argument in create_empty_array to np.zeros * Add get_attr and set_attr methods to VectorEnv * Improve consistency in SyncVectorEnv
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
import numpy as np
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import gym
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import time
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from gym.spaces import Box, Discrete, MultiDiscrete, MultiBinary, Tuple, Dict
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spaces = [
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Box(low=np.array(-1.), high=np.array(1.), dtype=np.float64),
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Box(low=np.array([0.]), high=np.array([10.]), dtype=np.float32),
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Box(low=np.array([-1., 0., 0.]), high=np.array([1., 1., 1.]), dtype=np.float32),
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Box(low=np.array([[-1., 0.], [0., -1.]]), high=np.ones((2, 2)), dtype=np.float32),
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Box(low=0, high=255, shape=(), dtype=np.uint8),
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Box(low=0, high=255, shape=(32, 32, 3), dtype=np.uint8),
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Discrete(2),
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Tuple((Discrete(3), Discrete(5))),
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Tuple((Discrete(7), Box(low=np.array([0., -1.]), high=np.array([1., 1.]), dtype=np.float32))),
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MultiDiscrete([11, 13, 17]),
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MultiBinary(19),
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Dict({
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'position': Discrete(23),
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'velocity': Box(low=np.array([0.]), high=np.array([1.]), dtype=np.float32)
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}),
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Dict({
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'position': Dict({'x': Discrete(29), 'y': Discrete(31)}),
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'velocity': Tuple((Discrete(37), Box(low=0, high=255, shape=(), dtype=np.uint8)))
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})
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]
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HEIGHT, WIDTH = 64, 64
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class UnittestSlowEnv(gym.Env):
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def __init__(self, slow_reset=0.3):
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super(UnittestSlowEnv, self).__init__()
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self.slow_reset = slow_reset
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self.observation_space = Box(low=0, high=255,
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shape=(HEIGHT, WIDTH, 3), dtype=np.uint8)
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self.action_space = Box(low=0., high=1., shape=(), dtype=np.float32)
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def reset(self):
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if self.slow_reset > 0:
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time.sleep(self.slow_reset)
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return self.observation_space.sample()
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def step(self, action):
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time.sleep(action)
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observation = self.observation_space.sample()
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reward, done = 0., False
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return observation, reward, done, {}
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def make_env(env_name, seed):
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def _make():
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env = gym.make(env_name)
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env.seed(seed)
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return env
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return _make
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def make_slow_env(slow_reset, seed):
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def _make():
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env = UnittestSlowEnv(slow_reset=slow_reset)
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env.seed(seed)
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return env
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return _make
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