README.md

Locomotion task library

This package contains reusable components for defining control tasks that are related to locomotion. New users are encouraged to start by browsing the examples/ subdirectory, which contains preconfigured RL environments associated with various research papers. These examples can serve as starting points or be customized to design new environments using the components available from this library.

Terminology

This library facilitates the creation of environments that require walkers to perform a task in an arena.

• walkers refer to detached bodies that can move around in the environment.

• arenas refer to the surroundings in which the walkers and possibly other objects exist.

• tasks refer to the specification of observations and rewards that are passed from the "environment" to the "agent", along with runtime details such as initialization and termination logic.

Installation and requirements

See the documentation for dm_control.

Quickstart

from dm_control import composer
from dm_control.locomotion.examples import basic_cmu_2019
import numpy as np

# Build an example environment.
env = basic_cmu_2019.cmu_humanoid_run_walls()

# Get the `action_spec` describing the control inputs.
action_spec = env.action_spec()

# Step through the environment for one episode with random actions.
time_step = env.reset()
while not time_step.last():
  action = np.random.uniform(action_spec.minimum, action_spec.maximum,
                             size=action_spec.shape)
  time_step = env.step(action)
  print("reward = {}, discount = {}, observations = {}.".format(
      time_step.reward, time_step.discount, time_step.observation))

dm_control.viewer can also be used to visualize and interact with the environment, e.g.:

from dm_control import viewer

viewer.launch(environment_loader=basic_cmu_2019.cmu_humanoid_run_walls)

Publications

This library contains environments that were adapted from several research papers. Relevant references include:

• Emergence of Locomotion Behaviours in Rich Environments (2017).

• Learning human behaviors from motion capture by adversarial imitation (2017).

• Hierarchical visuomotor control of humanoids (2019).

• Neural probabilistic motor primitives for humanoid control (2019).

• Deep neuroethology of a virtual rodent (2020).

• CoMic: Complementary Task Learning & Mimicry for Reusable Skills (2020)