Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL)

arXiv:2403.01059v1 Announce Type: new
Abstract: Machine-learning paradigms such as imitation learning and reinforcement learning can generate highly performant agents in a variety of complex environments. However, commonly used methods require large quantities of data and/or a known reward function. This paper presents a method called Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL) that employs a novel reward structure to improve the performance of imitation-learning agents that have access to only a handful of expert demonstrations. CMZ-DRIL uses reinforcement learning to minimize …

abstract agents arxiv continuous cs.lg data environments function generate imitation learning machine mean paper reinforcement reinforcement learning type