HAMMER: Multi-level Coordination Of Reinforcement Learning Agents Via Learned Messaging
2021 · Nikunj Gupta, G Srinivasaraghavan, Swarup Kumar Mohalik, et al.
Abstract
Cooperative multi-agent reinforcement learning (MARL) has achieved significant results, most notably by leveraging the representation-learning abilities of deep neural networks. However, large centralized approaches quickly become infeasible as the number of agents scale, and fully decentralized approaches can miss important opportunities for information sharing and coordination. Furthermore, not all agents are equal -- in some cases, individual agents may not even have the ability to send communication to other agents or explicitly model other agents. This paper considers the case where there is a single, powerful, *central agent* that can observe the entire observation space, and there are multiple, low-powered *local agents* that can only receive local observations and are not able to communicate with each other. The central agent's job is to learn what message needs to be sent to different local agents based on the global observations, not by centrally solving the entire problem an
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