gym_os2r_real.runtimes¶

gym_os2r_real.runtimes.realtime_runtime¶

class gym_os2r_real.runtimes.realtime_runtime.RealTimeRuntime(task_cls, agent_rate, task_mode, **kwargs)¶

Bases: gym_ignition.base.runtime.Runtime

Implementation of Runtime for real-time execution.

Warning

This class is not yet complete.

calibrate()¶

close()¶

Override close in your subclass to perform any necessary cleanup.

Environments will automatically close() themselves when garbage collected or when the program exits.

Return type: None

get_state_info(state, action)¶

property model: scenario.bindings.monopod.Model¶

Return type: Model

render(mode='human', **kwargs)¶

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

Return type: None

reset()¶

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

Note that this function should not reset the environment’s random number generator(s); random variables in the environment’s state should be sampled independently between multiple calls to reset(). In other words, each call of reset() should yield an environment suitable for a new episode, independent of previous episodes.

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

step(action)¶

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)

timestamp()¶

Return the timestamp associated to the execution of the environment.

In real-time environments, the timestamp is the time read from the host system. In simulated environments, the timestamp is the simulated time, which might not match the real-time in the case of a real-time factor different than 1.

Return type: float
Returns: The current environment timestamp.

property world: scenario.bindings.monopod.World¶

Return type: World

gym_os2r_real.runtimes.realtime_runtime.eprint(*args, **kwargs)¶