How to use the gym-unity.gym_unity.envs.unity_env.ActionFlattener function in gym-unity
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Unity-Technologies / ml-agents / gym-unity / gym_unity / envs / unity_env.py View on Github 
raise UnityGymException(
"There can only be one stacked vector observation in a UnityEnvironment "
"if it is wrapped in a gym."
)
# Check for number of agents in scene.
initial_info = self._env.reset()[self.brain_name]
self._check_agents(len(initial_info.agents))
# Set observation and action spaces
if brain.vector_action_space_type == "discrete":
if len(brain.vector_action_space_size) == 1:
self._action_space = spaces.Discrete(brain.vector_action_space_size[0])
else:
if flatten_branched:
self._flattener = ActionFlattener(brain.vector_action_space_size)
self._action_space = self._flattener.action_space
else:
self._action_space = spaces.MultiDiscrete(
brain.vector_action_space_size
)
else:
if flatten_branched:
logger.warning(
"The environment has a non-discrete action space. It will "
"not be flattened."
)
high = np.array([1] * brain.vector_action_space_size[0])
self._action_space = spaces.Box(-high, high, dtype=np.float32)
high = np.array([np.inf] * brain.vector_observation_space_size)
self.action_meanings = brain.vector_action_descriptionsraise UnityGymException(
"There can only be one stacked vector observation in a UnityEnvironment "
"if it is wrapped in a gym."
)
# Check for number of agents in scene.
initial_info = self._env.reset()[self.brain_name]
self._check_agents(len(initial_info.agents))
# Set observation and action spaces
if brain.vector_action_space_type == "discrete":
if len(brain.vector_action_space_size) == 1:
self._action_space = spaces.Discrete(brain.vector_action_space_size[0])
else:
if flatten_branched:
self._flattener = ActionFlattener(brain.vector_action_space_size)
self._action_space = self._flattener.action_space
else:
self._action_space = spaces.MultiDiscrete(
brain.vector_action_space_size
)
else:
if flatten_branched:
logger.warning(
"The environment has a non-discrete action space. It will "
"not be flattened."
)
high = np.array([1] * brain.vector_action_space_size[0])
self._action_space = spaces.Box(-high, high, dtype=np.float32)
high = np.array([np.inf] * brain.vector_observation_space_size)
self.action_meanings = brain.vector_action_descriptionsgym-unity
Unity Machine Learning Agents Gym Interface
