Off-Beat Multi-Agent Reinforcement Learning

Off-Beat Multi-Agent Reinforcement Learning

연구 분야: Artificial Intelligence

학회: AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

We investigate cooperative multi-agent reinforcement learning in environments with off-beat actions, i.e., all actions have execution durations. During execution durations, the environmental changes are not synchronised with action executions. To learn efficient multi-agent coordination in environments with off-beat actions, we propose a novel reward redistribution method built on our novel graph-based episodic memory. We name our solution method as LeGEM. Empirical results on stag-hunter game show that it significantly boosts multi-agent coordination.