deluca.envs.classic.MountainCar

class deluca.envs.classic.MountainCar(*args, **kwargs)

Public Data Attributes:

Inherited from Env

reward_range
action_space
observation_space
observation	assume observations are fully observable

Inherited from JaxObject

name
attrs

Inherited from Env

metadata
reward_range
spec
action_space
observation_space
unwrapped	Completely unwrap this env.

Public Methods:

__init__([goal_velocity, seed, horizon])	Initialize self.
step(action)	Run one timestep of the environment’s dynamics.
reset()	Resets the environment to an initial state and returns an initial observation.
render([mode])	Renders the environment.

Inherited from Env

__new__(cls, *args, **kwargs)	For avoiding super().__init__()
check_spaces()
__init_subclass__(*args, **kwargs)	For avoiding a decorator for each subclass
reset()	Resets the environment to an initial state and returns an initial observation.
dynamics(state, action)
check_action(action)
check_observation(observation)
step(action)	Run one timestep of the environment’s dynamics.
jacobian(func, state, action)
hessian(func, state, action)
close()	Override close in your subclass to perform any necessary cleanup.

Inherited from JaxObject

__new__(cls, *args, **kwargs)	For avoiding super().__init__()
__init_subclass__(*args, **kwargs)	For avoiding a decorator for each subclass
__str__()	Return str(self).
__setattr__(key, val)	Implement setattr(self, name, value).
save(path)
load(path)
throw(err, msg)

Inherited from Env

step(action)	Run one timestep of the environment’s dynamics.
reset()	Resets the environment to an initial state and returns an initial observation.
render([mode])	Renders the environment.
close()	Override close in your subclass to perform any necessary cleanup.
seed([seed])	Sets the seed for this env’s random number generator(s).
__str__()	Return str(self).
__enter__()	Support with-statement for the environment.
__exit__(*args)	Support with-statement for the environment.

Private Methods:

_height(xs)

---

__init__(goal_velocity=0, seed=0, horizon=50)

Initialize self. See help(type(self)) for accurate signature.

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 == ‘human’:

… # pop up a window and render

else:

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

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)