  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
