twodlearn.reinforce.modelica.modelica_env module

class twodlearn.reinforce.modelica.modelica_env.ModelicaEnv

Bases: gym.core.Env

close()

Override _close in your subclass to perform any necessary cleanup.

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

property dt

metadata = {'render.modes': ['human']}

property model

render(mode='human')

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 is ‘human’:

… # pop up a window and render

else:

super(MyEnv, self).render(mode=mode) # just raise an exception

reset()

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

Returns: observation (object): the initial observation of the

space.

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 environment

Returns

agent’s observation of the current environment reward (float) : amount of reward returned after previous action done (boolean): 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)

class twodlearn.reinforce.modelica.modelica_env.SignalRender(env)

Bases: object

do_render(data)

property model

class twodlearn.reinforce.modelica.modelica_env.StateRender(env)

Bases: object

do_render(data)

property model