| # Python Gym API Documentation |
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| <a name="mlagents_envs.envs.unity_gym_env"></a> |
| # mlagents\_envs.envs.unity\_gym\_env |
| |
| <a name="mlagents_envs.envs.unity_gym_env.UnityGymException"></a> |
| ## UnityGymException Objects |
| |
| ```python |
| class UnityGymException(error.Error) |
| ``` |
| |
| Any error related to the gym wrapper of ml-agents. |
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| <a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper"></a> |
| ## UnityToGymWrapper Objects |
| |
| ```python |
| class UnityToGymWrapper(gym.Env) |
| ``` |
| |
| Provides Gym wrapper for Unity Learning Environments. |
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| <a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.__init__"></a> |
| #### \_\_init\_\_ |
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| ```python |
| | __init__(unity_env: BaseEnv, uint8_visual: bool = False, flatten_branched: bool = False, allow_multiple_obs: bool = False, action_space_seed: Optional[int] = None) |
| ``` |
|
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| Environment initialization |
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| **Arguments**: |
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| - `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. |
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| <a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.reset"></a> |
| #### reset |
|
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| ```python |
| | reset(*, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) -> Tuple[np.ndarray, Dict] |
| ``` |
|
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| Resets the state of the environment and returns an initial observation and info. Returns: observation (object/list): the initial observation of the space. info (dict): contains auxiliary diagnostic information. |
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| <a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.step"></a> |
| #### step |
|
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| ```python |
| | step(action: Any) -> GymStepResult |
| ``` |
|
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| 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, terminated, truncated, info). |
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| **Arguments**: |
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| - `action` _object/list_ - an action provided by the environment |
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| **Returns**: |
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| - `observation` _object/list_ - agent's observation of the current environment reward (float/list) : amount of reward returned after previous action |
| - `terminated` _boolean/list_ - whether the episode has ended by termination. |
| - `truncated` _boolean/list_ - whether the episode has ended by truncation. |
| - `info` _dict_ - contains auxiliary diagnostic information. |
|
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| <a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.render"></a> |
| #### render |
|
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| ```python |
| | render() |
| ``` |
|
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| Return the latest visual observations. Note that it will not render a new frame of the environment. |
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| <a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.close"></a> |
| #### close |
|
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| ```python |
| | close() -> None |
| ``` |
|
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| Override _close in your subclass to perform any necessary cleanup. Environments will automatically close() themselves when garbage collected or when the program exits. |
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| <a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.seed"></a> |
| #### seed |
| |
| ```python |
| | 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> |
| ## ActionFlattener Objects |
| |
| ```python |
| class ActionFlattener() |
| ``` |
| |
| Flattens branched discrete action spaces into single-branch discrete action spaces. |
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| <a name="mlagents_envs.envs.unity_gym_env.ActionFlattener.__init__"></a> |
| #### \_\_init\_\_ |
|
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| ```python |
| | __init__(branched_action_space) |
| ``` |
|
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| Initialize the flattener. |
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| **Arguments**: |
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| - `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. |
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| <a name="mlagents_envs.envs.unity_gym_env.ActionFlattener.lookup_action"></a> |
| #### lookup\_action |
| |
| ```python |
| | lookup_action(action) |
| ``` |
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| Convert a scalar discrete action into a unique set of branched actions. |
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| **Arguments**: |
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| - `action`: A scalar value representing one of the discrete actions. |
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| **Returns**: |
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| The List containing the branched actions. |
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