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a4473a4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | # 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.
|