garage.envs.point_env module¶
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class
PointEnv
(goal=array([1., 1.], dtype=float32), done_bonus=0.0, never_done=False)[source]¶ Bases:
gym.core.Env
A simple 2D point environment.
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observation_space
¶ The observation space
Type: gym.spaces.Box
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action_space
¶ The action space
Type: gym.spaces.Box
Parameters: -
action_space
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observation_space
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render
(mode='human')[source]¶ Renders the environment.
The set of supported modes varies per environment. (And some environments do not support rendering at all.) By convention, if mode is:
- human: render to the current display or terminal and return nothing. Usually for human consumption.
- rgb_array: Return an numpy.ndarray with shape (x, y, 3), representing RGB values for an x-by-y pixel image, suitable for turning into a video.
- ansi: Return a string (str) or StringIO.StringIO containing a terminal-style text representation. The text can include newlines and ANSI escape sequences (e.g. for colors).
Note
- Make sure that your class’s metadata ‘render.modes’ key includes
- the list of supported modes. It’s recommended to call super() in implementations to use the functionality of this method.
Parameters: mode (str) – the mode to render with Example:
- class MyEnv(Env):
metadata = {‘render.modes’: [‘human’, ‘rgb_array’]}
- def render(self, mode=’human’):
- if mode == ‘rgb_array’:
- return np.array(…) # return RGB frame suitable for video
- elif mode == ‘human’:
- … # pop up a window and render
- else:
- super(MyEnv, self).render(mode=mode) # just raise an exception
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reset
()[source]¶ Resets the state of the environment and returns an initial observation.
Returns: the initial observation. Return type: observation (object)
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step
(action)[source]¶ 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, done, info).
Parameters: action (object) – an action provided by the agent Returns: agent’s observation of the current environment reward (float) : amount of reward returned after previous action done (bool): whether the episode has ended, in which case further step() calls will return undefined results info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning) Return type: observation (object)
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