A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character Control. (arXiv:2105.10066v4 [cs.GR] UPDATED)

We present a simple and intuitive approach for interactive control of
physically simulated characters. Our work builds upon generative adversarial
networks (GAN) and reinforcement learning, and introduces an imitation learning
framework where an ensemble of classifiers and an imitation policy are trained
in tandem given pre-processed reference clips. The classifiers are trained to
discriminate the reference motion from the motion generated by the imitation
policy, while the policy is rewarded for fooling the discriminators. Using our
GAN-based approach, multiple motor …

arxiv gan interactive learning physics