docs/Python-Gym-API-Documentation.md
<a name="mlagents_envs.envs.unity_gym_env"></a>
<a name="mlagents_envs.envs.unity_gym_env.UnityGymException"></a>
class UnityGymException(error.Error)
Any error related to the gym wrapper of ml-agents.
<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper"></a>
class UnityToGymWrapper(gym.Env)
Provides Gym wrapper for Unity Learning Environments.
<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.__init__"></a>
| __init__(unity_env: BaseEnv, uint8_visual: bool = False, flatten_branched: bool = False, allow_multiple_obs: bool = False, action_space_seed: Optional[int] = None)
Environment initialization
Arguments:
unity_env: The Unity BaseEnv to be wrapped in the gym. Will be closed when the UnityToGymWrapper closes.uint8_visual: Return visual observations as uint8 (0-255) matrices instead of float (0.0-1.0).flatten_branched: If True, turn branched discrete action spaces into a Discrete space rather than
MultiDiscrete.allow_multiple_obs: If True, return a list of np.ndarrays as observations with the first elements
containing the visual observations and the last element containing the array of vector observations.
If False, returns a single np.ndarray containing either only a single visual observation or the array of
vector observations.action_space_seed: If non-None, will be used to set the random seed on created gym.Space instances.<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.reset"></a>
| reset() -> Union[List[np.ndarray], np.ndarray]
Resets the state of the environment and returns an initial observation. Returns: observation (object/list): the initial observation of the space.
<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.step"></a>
| step(action: List[Any]) -> GymStepResult
Run one timestep of the environment's dynamics. When end of
episode is reached, you are responsible for calling reset()
to reset this environment's state.
Accepts an action and returns a tuple (observation, reward, done, info).
Arguments:
action object/list - an action provided by the environmentReturns:
observation object/list - agent's observation of the current environment
reward (float/list) : amount of reward returned after previous actiondone boolean/list - whether the episode has ended.info dict - contains auxiliary diagnostic information.<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.render"></a>
| render(mode="rgb_array")
Return the latest visual observations. Note that it will not render a new frame of the environment.
<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.close"></a>
| close() -> None
Override _close in your subclass to perform any necessary cleanup. Environments will automatically close() themselves when garbage collected or when the program exits.
<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.seed"></a>
| seed(seed: Any = None) -> None
Sets the seed for this env's random number generator(s). Currently not implemented.
<a name="mlagents_envs.envs.unity_gym_env.ActionFlattener"></a>
class ActionFlattener()
Flattens branched discrete action spaces into single-branch discrete action spaces.
<a name="mlagents_envs.envs.unity_gym_env.ActionFlattener.__init__"></a>
| __init__(branched_action_space)
Initialize the flattener.
Arguments:
branched_action_space: A List containing the sizes of each branch of the action
space, e.g. [2,3,3] for three branches with size 2, 3, and 3 respectively.<a name="mlagents_envs.envs.unity_gym_env.ActionFlattener.lookup_action"></a>
| lookup_action(action)
Convert a scalar discrete action into a unique set of branched actions.
Arguments:
action: A scalar value representing one of the discrete actions.Returns:
The List containing the branched actions.