Deep Multi-Agent Reinforcement Learning with Relevance Graphs

Aleksandra Malysheva; Tegg Taekyong Sung; Chae-Bong Sohn; Daniel Kudenko; Aleksei Shpilman

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arXiv · 1811.12557

Deep Multi-Agent Reinforcement Learning with Relevance Graphs

Aleksandra Malysheva·Tegg Taekyong Sung·Chae-Bong Sohn·Daniel Kudenko·Aleksei Shpilman

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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 (MARL) that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique inspired by the NerveNet architecture. We applied our MAGnet approach to the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including DQN, MADDPG, and MCTS.

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Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn, Daniel Kudenko, Aleksei Shpilman. 2018-11-30. Deep Multi-Agent Reinforcement Learning with Relevance Graphs. https://arxiv.org/abs/1811.12557

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