CODA - Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning

Abstract

We introduce CODA (Coordination via On-Policy Diffusion for Multi-Agent Reinforcement Learning), a diffusion-based multi-agent trajectory generator for data augmentation that samples conditioned on the current joint policy, producing synthetic experience which better reflects the evolving behaviours of the agents, thereby providing a mechanism for co-adaptation.