com.unity.ml-agents/Documentation~/Python-Gym-API-Documentation.md

# Python Gym API Documentation

<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.

<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper"></a>
## UnityToGymWrapper Objects

```python
class UnityToGymWrapper(gym.Env)
```

Provides Gym wrapper for Unity Learning Environments.

<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.__init__"></a>
#### \_\_init\_\_

```python
 | __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

```python
 | reset(*, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) -> Tuple[np.ndarray, Dict]
```

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.

<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.step"></a>
#### step

```python
 | step(action: 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, terminated, truncated, info).

**Arguments**:

- `action` _object/list_ - an action provided by the environment

**Returns**:

- `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.

<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.render"></a>
#### render

```python
 | render()
```

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

```python
 | 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

```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.

<a name="mlagents_envs.envs.unity_gym_env.ActionFlattener.__init__"></a>
#### \_\_init\_\_

```python
 | __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

```python
 | 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.