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<li class="toctree-l3"><a href="#using-the-openai-gym-api">Using the OpenAI Gym API</a></li>
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<p>Adding a new environment to Coach is as easy as solving CartPole. </p>
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<p>There are essentially two ways to integrate new environments to Coach:</p>
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<h2 id="using-the-openai-gym-api">Using the OpenAI Gym API</h2>
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<p>If your environment is already using the OpenAI Gym API, you are already good to go.
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When selecting the environment parameters in the preset, use GymEnvironmentParameters(),
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and pass the path to your environment source code using the level parameter.
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You can specify additional parameters for your environment using the additional_simulator_parameters parameter.
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Take for example the definition used in the Pendulum_HAC preset:</p>
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<pre><code> env_params = GymEnvironmentParameters()
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env_params.level = "rl_coach.environments.mujoco.pendulum_with_goals:PendulumWithGoals"
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env_params.additional_simulator_parameters = {"time_limit": 1000}
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</code></pre>
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<h2 id="using-the-coach-api">Using the Coach API</h2>
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<p>There are a few simple steps to follow, and we will walk through them one by one.</p>
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<ol>
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<li>
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<p>Create a new class for your environment, and inherit the Environment class.</p>
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</li>
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<li>
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<p>Coach defines a simple API for implementing a new environment, which are defined in environment/environment.py.
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There are several functions to implement, but only some of them are mandatory.</p>
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<p>Here are the important ones:</p>
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<pre><code> def _take_action(self, action_idx: ActionType) -> None:
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"""
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An environment dependent function that sends an action to the simulator.
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:param action_idx: the action to perform on the environment
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:return: None
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"""
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def _update_state(self) -> None:
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"""
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Updates the state from the environment.
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Should update self.observation, self.reward, self.done, self.measurements and self.info
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:return: None
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"""
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def _restart_environment_episode(self, force_environment_reset=False) -> None:
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"""
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Restarts the simulator episode
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:param force_environment_reset: Force the environment to reset even if the episode is not done yet.
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:return: None
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"""
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def _render(self) -> None:
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"""
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Renders the environment using the native simulator renderer
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:return: None
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"""
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def get_rendered_image(self) -> np.ndarray:
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"""
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Return a numpy array containing the image that will be rendered to the screen.
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This can be different from the observation. For example, mujoco's observation is a measurements vector.
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:return: numpy array containing the image that will be rendered to the screen
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"""
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</code></pre>
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</li>
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<li>
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<p>Create a new parameters class for your environment, which inherits the EnvironmentParameters class.
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In the <strong>init</strong> of your class, define all the parameters you used in your Environment class.
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Additionally, fill the path property of the class with the path to your Environment class.
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For example, take a look at the EnvironmentParameters class used for Doom:</p>
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<pre><code> class DoomEnvironmentParameters(EnvironmentParameters):
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def __init__(self):
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super().__init__()
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self.default_input_filter = DoomInputFilter
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self.default_output_filter = DoomOutputFilter
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self.cameras = [DoomEnvironment.CameraTypes.OBSERVATION]
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@property
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def path(self):
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return 'rl_coach.environments.doom_environment:DoomEnvironment'
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</code></pre>
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</li>
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<li>
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<p>And that's it, you're done. Now just add a new preset with your newly created environment, and start training an agent on top of it.</p>
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