Keywords: hierarchical multi-agent reinforcement learning, intention, communication, attention, behavior inference
TL;DR: This paper proposes a new hierarchical multi-agent cooperation framework which leverages long short-term intention to improve agents' coordination
Abstract: Communication is a significant method to relieve partial observable and non-stationary problems in Multi-Agent System. However, most of existing work needs to persistently communicate by exchanging partial observation or latent embeddings (intention) which is unrealistic in real-world settings. To overcome this problem, we propose learning hierarchical multi-agent cooperation with long short-term intention (HLSI), a hierarchical multi-agent cooperation framework. In our work, each agent communicates by sharing high-level policy's latent embeddings (long-term intention) which keeps contant until macro action change. To make the communication messages contain more useful content, we maximize mutual information between agent's macro action and agent's future trajectory conditioned on historical trajectory. Agent integrates these messages through the attention mechanism. Then, long short-term intention fusion module will fuse the long-term intention received from other agents and short-term intention inferred by a behaivor inference network to approximate other agents' real short-term intention, which helps agent better understand others' next behavior. We provide comprehensive evaluation and ablations studies in multi-agent cooperative settings. The results show that our method achieves better performance than other multi-agent communication and hierarchical multi-agent reinforcement learning baselines.
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