garage.envs.mujoco.half_cheetah_env_meta_base

Base class of HalfCheetah meta-environments.

class HalfCheetahEnvMetaBase(task)

Bases: gym.envs.mujoco.HalfCheetahEnv

Inheritance diagram of garage.envs.mujoco.half_cheetah_env_meta_base.HalfCheetahEnvMetaBase

Base class of HalfCheetah meta-environments.

Code is adapted from https://github.com/tristandeleu/pytorch-maml-rl/blob/493e677e724aa67a531250b0e215c8dbc9a7364a/maml_rl/envs/mujoco/half_cheetah.py

Which was in turn adapted from https://github.com/cbfinn/maml_rl/blob/9c8e2ebd741cb0c7b8bf2d040c4caeeb8e06cc95/rllab/envs/mujoco/half_cheetah_env_rand.py

Parameters:task (dict) – Subclass specific task information.
dt
metadata
reward_range
spec
action_space
observation_space
unwrapped

Completely unwrap this env.

Returns:The base non-wrapped gym.Env instance
Return type:gym.Env
viewer_setup(self)

Start the viewer.

step(self, 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)
reset_model(self)

Reset the robot degrees of freedom (qpos and qvel). Implement this in each subclass.

seed(self, seed=None)

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:
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.
Return type:list<bigint>
reset(self)

Resets the state of the environment and returns an initial observation.

Returns:the initial observation.
Return type:observation (object)
set_state(self, qpos, qvel)
do_simulation(self, ctrl, n_frames)
render(self, mode='human', width=DEFAULT_SIZE, height=DEFAULT_SIZE, camera_id=None, camera_name=None)

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
close(self)

Override close in your subclass to perform any necessary cleanup.

Environments will automatically close() themselves when garbage collected or when the program exits.

get_body_com(self, body_name)
state_vector(self)