gym_donkeycar.envs package¶
Submodules¶
gym_donkeycar.envs.donkey_env module¶
file: donkey_env.py author: Tawn Kramer date: 2018-08-31
-
class
gym_donkeycar.envs.donkey_env.DonkeyEnv(level, time_step=0.05, frame_skip=2)[source]¶ Bases:
gym.core.EnvOpenAI Gym Environment for Donkey
-
ACTION_NAMES= ['steer', 'throttle']¶
-
STEER_LIMIT_LEFT= -1.0¶
-
STEER_LIMIT_RIGHT= 1.0¶
-
THROTTLE_MAX= 5.0¶
-
THROTTLE_MIN= 0.0¶
-
VAL_PER_PIXEL= 255¶
-
close()[source]¶ Override close in your subclass to perform any necessary cleanup.
Environments will automatically close() themselves when garbage collected or when the program exits.
-
metadata= {'render.modes': ['human', 'rgb_array']}¶
-
render(mode='human', close=False)[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.
- Args:
- 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:
- observation (object): the initial observation.
-
seed(seed=None)[source]¶ Sets the seed for this env’s random number generator(s).
- Note:
- Some environments use multiple pseudorandom number generators. We want to capture all such seeds used in order to ensure that there aren’t accidental correlations between multiple generators.
- Returns:
- list<bigint>: Returns the list of seeds used in this env’s random
- number generators. The first value in the list should be the “main” seed, or the value which a reproducer should pass to ‘seed’. Often, the main seed equals the provided ‘seed’, but this won’t be true if seed=None, for example.
-
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).
- Args:
- action (object): an action provided by the agent
- Returns:
- observation (object): 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)
-
gym_donkeycar.envs.donkey_ex module¶
gym_donkeycar.envs.donkey_proc module¶
file: donkey_proc.py author: Felix Yu date: 2018-09-12
gym_donkeycar.envs.donkey_sim module¶
file: donkey_sim.py author: Tawn Kramer date: 2018-08-31
-
class
gym_donkeycar.envs.donkey_sim.DonkeyUnitySimContoller(level, time_step=0.05, hostname='0.0.0.0', port=9090, max_cte=5.0, loglevel='INFO', cam_resolution=(120, 160, 3))[source]¶ Bases:
object