garage.envs.bullet

Wrappers for the py_bullet based gym environments.

See https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet

class BulletEnv(env, is_image=False, max_episode_length=None)

Bases: garage.envs.GymEnv

Inheritance diagram of garage.envs.bullet.BulletEnv

Binding for py_bullet environments.

action_space

The action space specification.

Type:akro.Space
observation_space

The observation space specification.

Type:akro.Space
spec

The envionrment specification.

Type:garage.envs.env_spec.EnvSpec
render_modes

A list of string representing the supported render modes.

Type:list
close(self)

Close the wrapped env.

reset(self)

Call reset on wrapped env.

Returns:
The first observation conforming to
observation_space.
dict: The episode-level information.
Note that this is not part of env_info provided in step(). It contains information of he entire episode， which could be needed to determine the first action (e.g. in the case of goal-conditioned or MTRL.)
Return type:numpy.ndarray
step(self, action)

Call step on wrapped env.

Parameters:action (np.ndarray) – An action provided by the agent.
Returns:The environment step resulting from the action.
Return type:EnvStep
Raises:RuntimeError – if step() is called after the environment has been constructed and reset() has not been called.
render(self, mode)

Renders the environment.

Parameters:mode (str) – the mode to render with. The string must be present in self.render_modes.
Returns:the return value for render, depending on each env.
Return type:object
visualize(self)

Creates a visualization of the environment.