Latent Policies for Adversarial Imitation Learning. (arXiv:2206.11299v1 [cs.LG])

Web: http://arxiv.org/abs/2206.11299

This paper considers learning robot locomotion and manipulation tasks from
expert demonstrations. Generative adversarial imitation learning (GAIL) trains
a discriminator that distinguishes expert from agent transitions, and in turn
use a reward defined by the discriminator output to optimize a policy generator
for the agent. This generative adversarial training approach is very powerful
but depends on a delicate balance between the discriminator and the generator
training. In high-dimensional problems, the discriminator training may easily
overfit or exploit associations with task-irrelevant …

arxiv imitation learning learning lg