The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
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# Table of Contents
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* [mlagents\_envs.envs.unity\_gym\_env](#mlagents_envs.envs.unity_gym_env)
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* [UnityGymException](#mlagents_envs.envs.unity_gym_env.UnityGymException)
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* [UnityToGymWrapper](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper)
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* [\_\_init\_\_](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.__init__)
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* [reset](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.reset)
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* [step](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.step)
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* [render](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.render)
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* [close](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.close)
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* [seed](#mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.seed)
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* [ActionFlattener](#mlagents_envs.envs.unity_gym_env.ActionFlattener)
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* [\_\_init\_\_](#mlagents_envs.envs.unity_gym_env.ActionFlattener.__init__)
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* [lookup\_action](#mlagents_envs.envs.unity_gym_env.ActionFlattener.lookup_action)
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<a name="mlagents_envs.envs.unity_gym_env"></a>
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# mlagents\_envs.envs.unity\_gym\_env
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<a name="mlagents_envs.envs.unity_gym_env.UnityGymException"></a>
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## UnityGymException Objects
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```python
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class UnityGymException(error.Error)
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```
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Any error related to the gym wrapper of ml-agents.
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<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper"></a>
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## UnityToGymWrapper Objects
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```python
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class UnityToGymWrapper(gym.Env)
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```
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Provides Gym wrapper for Unity Learning Environments.
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<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.__init__"></a>
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#### \_\_init\_\_
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```python
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| __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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```
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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.
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- `uint8_visual`: Return visual observations as uint8 (0-255) matrices instead of float (0.0-1.0).
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- `flatten_branched`: If True, turn branched discrete action spaces into a Discrete space rather than
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MultiDiscrete.
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- `allow_multiple_obs`: If True, return a list of np.ndarrays as observations with the first elements
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containing the visual observations and the last element containing the array of vector observations.
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If False, returns a single np.ndarray containing either only a single visual observation or the array of
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vector observations.
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- `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>
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#### reset
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```python
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| reset() -> Union[List[np.ndarray], np.ndarray]
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```
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Resets the state of the environment and returns an initial observation.
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Returns: observation (object/list): the initial observation of the
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space.
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<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.step"></a>
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#### step
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```python
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| step(action: List[Any]) -> GymStepResult
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```
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Run one timestep of the environment's dynamics. When end of
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episode is reached, you are responsible for calling `reset()`
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to reset this environment's state.
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Accepts an action and returns a tuple (observation, reward, done, 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
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reward (float/list) : amount of reward returned after previous action
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- `done` _boolean/list_ - whether the episode has ended.
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- `info` _dict_ - contains auxiliary diagnostic information.
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<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.render"></a>
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#### render
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```python
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| render(mode="rgb_array")
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```
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Return the latest visual observations.
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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>
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#### close
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```python
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| close() -> None
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```
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Override _close in your subclass to perform any necessary cleanup.
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Environments will automatically close() themselves when
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garbage collected or when the program exits.
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<a name="mlagents_envs.envs.unity_gym_env.UnityToGymWrapper.seed"></a>
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#### seed
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```python
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| seed(seed: Any = None) -> None
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```
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Sets the seed for this env's random number generator(s).
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Currently not implemented.
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<a name="mlagents_envs.envs.unity_gym_env.ActionFlattener"></a>
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## ActionFlattener Objects
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```python
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class ActionFlattener()
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```
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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>
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#### \_\_init\_\_
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```python
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| __init__(branched_action_space)
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```
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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
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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>
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#### lookup\_action
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```python
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| lookup_action(action)
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```
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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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