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<li>Adding a New Environment</li>
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<div role="main">
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<div class="section">
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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 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>Coach defines a simple API for implementing a new environment which is defined in environment/environment_wrapper.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):
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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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pass
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def _preprocess_observation(self, observation):
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"""
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Do initial observation preprocessing such as cropping, rgb2gray, rescale etc.
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Implementing this function is optional.
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:param observation: a raw observation from the environment
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:return: the preprocessed observation
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"""
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return observation
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def _update_state(self):
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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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pass
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def _restart_environment_episode(self, force_environment_reset=False):
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"""
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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:
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"""
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pass
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def get_rendered_image(self):
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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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return self.observation
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</code></pre>
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</li>
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<li>
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<p>Make sure to import the environment in environments/__init__.py:</p>
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<pre><code>from doom_environment_wrapper import *
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</code></pre>
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<p>Also, a new entry should be added to the EnvTypes enum mapping the environment name to the wrapper's class name:</p>
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<pre><code>Doom = "DoomEnvironmentWrapper"
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</code></pre>
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</li>
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<li>
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<p>In addition a new configuration class should be implemented for defining the environment's parameters and placed in configurations.py.
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For instance, the following is used for Doom:</p>
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<pre><code>class Doom(EnvironmentParameters):
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type = 'Doom'
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frame_skip = 4
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observation_stack_size = 3
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desired_observation_height = 60
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desired_observation_width = 76
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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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