Zhongxiang Ling

Multi-Agent Imitation by Learning and Sampling from Factorized Soft Q-Function

Learning from multi-agent expert demonstrations, known as Multi-Agent Imitation Learning (MAIL), provides a promising approach to sequential decision-making. However, existing MAIL methods including Behavior Cloning (BC) and Adversarial Imitation Learning (AIL) face significant challenges: BC suffer…