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

• URL: http://arxiv.org/abs/2012.09762v1
• Date: Thu, 17 Dec 2020 17:19:36 GMT
• Title: MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
• Authors: Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
• Abstract summary: We propose a novel approach, called MAGnet, to multi-agent reinforcement learning. We show that it significantly outperforms state-of-the-art MARL solutions.
• Score: 70.540936204654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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