twodlearn.reinforce.models.bullet_env module

class twodlearn.reinforce.models.bullet_env.SingleRobotBulletEnv(BulletRobot, frame_skip=2)

Bases: pybullet_envs.env_bases.MJCFBaseBulletEnv

calc_done(state, action)

calc_reward(state, action)

camera_adjust()

create_single_player_scene(bullet_client)

property dt

property frame_skip

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)