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Regular version of the site

Book chapter

MAGNet: Multi-Agent Graph Network for Deep Multi-Agent Reinforcement Learning

P. 171-176.
Shpilman A., Malysheva A., Kudenko D.

Over recent years, deep reinforcement learning has shown strong successes in complex single-Agent tasks, and more recently this approach has also been applied to multi-Agent domains. In this paper, we propose a novel approach, called MAGNet, to multi-Agent reinforcement learning that utilizes a relevance graph representation of the environment obtained by a self-Attention mechanism, and a message-generation technique. We applied our MAGnet approach to the synthetic predator-prey multi-Agent environment and the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including Multi-Agent Deep Q-Networks (MADQN), Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and QMIX.