  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.
