The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation.
We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions.
Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system.
We demonstrate this is an issue for current agents, where even matching the compute used for training is sometimes insufficient for evaluation.
We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent.
The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals.
Solving this difficult and practically relevant problem in the real world is an important long-term goal for the field of robotics.
When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way.
One of these variants, SVG(1), shows the effectiveness of learning models, value functions, and policies simultaneously in continuous domains.
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.