garage.envs.point_env¶
Simple 2D environment containing a point and a goal location.
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class
PointEnv(goal=np.array((1.0, 1.0), dtype=np.float32), arena_size=5.0, done_bonus=0.0, never_done=False, max_episode_length=math.inf)¶ Bases:
garage.Environment
A simple 2D point environment.
Parameters: - goal (np.ndarray) – A 2D array representing the goal position
- arena_size (float) – The size of arena where the point is constrained within (-arena_size, arena_size) in each dimension
- done_bonus (float) – A numerical bonus added to the reward once the point as reached the goal
- never_done (bool) – Never send a done signal, even if the agent achieves the goal
- max_episode_length (int) – The maximum steps allowed for an episode.
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action_space¶ The action space specification.
Type: akro.Space
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observation_space¶ The observation space specification.
Type: akro.Space
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reset(self)¶ Reset the environment.
Returns: - The first observation conforming to
- observation_space.
- dict: The episode-level information.
- Note that this is not part of env_info provided in step(). It contains information of he entire episode， which could be needed to determine the first action (e.g. in the case of goal-conditioned or MTRL.)
Return type: numpy.ndarray
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step(self, action)¶ Step the environment.
Parameters: action (np.ndarray) – An action provided by the agent.
Returns: The environment step resulting from the action.
Return type: Raises: RuntimeError– if step() is called after the environment- has been – constructed and reset() has not been called.
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render(self, mode)¶ Renders the environment.
Parameters: mode (str) – the mode to render with. The string must be present in self.render_modes. Returns: the point and goal of environment. Return type: str
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visualize(self)¶ Creates a visualization of the environment.
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close(self)¶ Close the env.