garage.envs.point_env module

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.

observation_space

The observation space

Type:gym.spaces.Box
action_space

The action space

Type:gym.spaces.Box
Parameters:
  • goal (np.ndarray, optional) – A 2D array representing the goal position
  • done_bonus (float, optional) – A numerical bonus added to the reward once the point as reached the goal
  • never_done (bool, optional) – Never send a done signal, even if the agent achieves the goal.
action_space
observation_space
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
reset()[source]

Resets the state of the environment and returns an initial observation.

Returns:the initial observation.
Return type:observation (object)
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)