garage.envs.point_env module¶
Simple 2D environment containing a point and a goal location.
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class
PointEnv
(goal=array([1., 1.], dtype=float32), arena_size=5.0, 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: - 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
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action_space
The action space.
Type: gym.spaces.Box
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observation_space
The observation space.
Type: gym.spaces.Box
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render
(mode='human')[source]¶ Draw the environment.
Not implemented.
Parameters: mode (str) – Ignored.
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reset
()[source]¶ Reset the environment.
Returns: Observation of the environment. Return type: np.ndarray
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sample_tasks
(num_tasks)[source]¶ Sample a list of num_tasks tasks.
Parameters: num_tasks (int) – Number of tasks to sample. Returns: - A list of “tasks”, where each task is
- a dictionary containing a single key, “goal”, mapping to a point in 2D space.
Return type: list[dict[str, np.ndarray]]
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set_task
(task)[source]¶ Reset with a task.
Parameters: task (dict[str, np.ndarray]) – A task (a dictionary containing a single key, “goal”, which should be a point in 2D space).
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step
(action)[source]¶ Step the environment state.
Parameters: action (np.ndarray) – The action to take in the environment. Returns: Observation. The observation of the environment. float: Reward. The reward acquired at this time step. boolean: Done. Whether the environment was completed at this time step. Always False for this environment.Return type: np.ndarray
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