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.

close(self)

Close the wrapped env.

property action_space(self)

akro.Space: The action space specification.

property observation_space(self)

akro.Space: The observation space specification.

property spec(self)

garage.envs.env_spec.EnvSpec: The envionrment specification.

property render_modes(self)

list: A list of string representing the supported render modes.

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.