mirror of
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
60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
import gym
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from gym.spaces import Tuple
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from gym.vector.utils.spaces import batch_space
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__all__ = ['VectorEnv']
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class VectorEnv(gym.Env):
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"""Base class for vectorized environments.
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Parameters
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----------
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num_envs : int
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Number of environments in the vectorized environment.
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observation_space : `gym.spaces.Space` instance
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Observation space of a single environment.
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action_space : `gym.spaces.Space` instance
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Action space of a single environment.
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"""
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def __init__(self, num_envs, observation_space, action_space):
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super(VectorEnv, self).__init__()
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self.num_envs = num_envs
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self.observation_space = batch_space(observation_space, n=num_envs)
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self.action_space = Tuple((action_space,) * num_envs)
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self.closed = False
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self.viewer = None
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# The observation and action spaces of a single environment are
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# kept in separate properties
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self.single_observation_space = observation_space
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self.single_action_space = action_space
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def reset_async(self):
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pass
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def reset_wait(self, **kwargs):
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raise NotImplementedError()
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def reset(self):
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self.reset_async()
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return self.reset_wait()
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def step_async(self, actions):
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pass
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def step_wait(self, **kwargs):
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raise NotImplementedError()
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def step(self, actions):
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self.step_async(actions)
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return self.step_wait()
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def __del__(self):
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if hasattr(self, 'closed'):
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if not self.closed:
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self.close()
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