Graph-based environments pose unique challenges to multi-agent reinforcement
learning. In decentralized approaches, agents operate within a given graph and
make decisions based on partial or outdated observations. The size of the
observed neighborhood limits the generalizability to different graphs and
affects the reactivity of agents, the quality of the selected actions, and the
communication overhead. This work focuses on generalizability and resolves the
trade-off in observed neighborhood size with a continuous information flow in
the whole graph. We propose a recurrent message-passing model that iterates
with the environment's steps and allows nodes to create a global representation
of the graph by exchanging messages with their neighbors. Agents receive the
resulting learned graph observations based on their location in the graph. Our
approach can be used in a decentralized manner at runtime and in combination
with a reinforcement learning algorithm of choice. We evaluate our method
across 1000 diverse graphs in the context of routing in communication networks
and find that it enables agents to generalize and adapt to changes in the
graph.

该研究论文探讨了在图形环境中基于图的多智能体强化学习所面临的挑战，并提出了一种通过与周围节点交换信息来创建全局图表示的循环传递信息模型，并在通信网络中的路由上验证了该方法，发现它使智能体能够泛化和适应图中的变化。