Lujuan Dang

Efficient Heterogeneous Exploration with Mutual Policy Divergence Maximization for Multiagent Reinforcement Learning.

This work introduces a novel MARL framework, Multi-Agent Divergence Policy Optimization (MADPO) with Mutual Policy Divergence Maximization (Mutual PDM), and proposes a new extension of CCS divergence for measuring policy divergence of more than two agents, the Generalized Conditional Cauchy-Schwarz (GCCS) divergence.