This submodule contains the domains and tasks described in the DeepMind Control Suite tech report.

from dm_control import suite
import numpy as np

# Load one task:
env = suite.load(domain_name="cartpole", task_name="swingup")

# Iterate over a task set:
for domain_name, task_name in suite.BENCHMARKING:
  env = suite.load(domain_name, task_name)

# Step through an episode and print out reward, discount and observation.
action_spec = env.action_spec()
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(time_step.reward, time_step.discount, time_step.observation)

Below is a video montage of solved Control Suite tasks, with reward visualisation enabled.

Roughly based on the 'ant' model introduced by Schulman et al. 2015. Main modifications to the body are:

• 4 DoFs per leg, 1 constraining tendon.
• 3 actuators per leg: 'yaw', 'lift', 'extend'.
• Filtered position actuators with timescale of 100ms.
• Sensors include an IMU, force/torque sensors, and rangefinders.

Four tasks:

• walk and run: self-right the body then move forward at a desired speed.
• escape: escape a bowl-shaped random terrain (uses rangefinders).
• fetch, go to a moving ball and bring it to a target.

All behaviors in the video below were trained with Abdolmaleki et al's MPO.