MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning

arXiv:2304.06011v2 Announce Type: replace
Abstract: Multi-agent reinforcement learning (MARL) methods often suffer from high sample complexity, limiting their use in real-world problems where data is sparse or expensive to collect. Although latent-variable world models have been employed to address this issue by generating abundant synthetic data for MARL training, most of these models cannot encode vital global information available during training into their latent states, which hampers learning efficiency. The few exceptions that incorporate global information assume centralized execution of …

abstract agent arxiv complexity cs.lg cs.ma data issue multi-agent reinforcement reinforcement learning sample synthetic synthetic data training type world world models