text: Learning human behaviors from motion capture by adversarial imitation
  
  Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang,
  Greg Wayne, Nicolas Heess
  
  > DeepMind
  
  Rapid progress in deep reinforcement learning has made it increasingly feasible
  to train controllers for high-dimensional humanoid bodies. However, methods
  that use pure reinforcement learning with simple reward functions tend to produce
  non-humanlike and overly stereotyped movement behaviors. In this work,
  we extend generative adversarial imitation learning to enable training of generic
  neural network policies to produce humanlike movement patterns from limited
  demonstrations consisting only of partially observed state features, without access
  to actions, even when the demonstrations come from a body with different and
  unknown physical parameters. We leverage this approach to build sub-skill policies
  from motion capture data and show that they can be reused to solve tasks when
  controlled by a higher level controller. [video abstract]
  