Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning

arXiv:2402.17978v1 Announce Type: new
Abstract: Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intrinsic rewards to enable committed exploration or use role-based learning for decomposing joint action spaces instead of directly conducting a collective search in the entire action-observation space. However, they often face challenges obtaining specific joint action sequences to reach successful states in long-horizon tasks. To address this limitation, we propose Imagine, Initialize, and Explore …

abstract agent arxiv collective cs.ai cs.lg cs.ma exploration explore imagine intrinsic multi-agent reinforcement reinforcement learning role search spaces strategies tasks type