Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing

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 In decentralized approaches, agents operate within a given graph and make decisions based on partial or outdated observations.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.

Learning to Cooperate and Communicate Over Imperfect Channels

Information exchange in multi-agent systems improves the cooperation among agents, especially in partially observable settings. In the real world, communication is often carried out over imperfect channels. This requires agents to handle uncertainty due to potential information loss. In this paper, we consider a cooperative multi-agent system where the agents act and exchange information in a decentralized manner using a limited and unreliable channel. To cope with such channel constraints, we propose a novel communication approach based on independent Q-learning. Our method allows agents to dynamically adapt how much information to share by sending messages of different sizes, depending on their local observations and the channel's properties. In addition to this message size selection, agents learn to encode and decode messages to improve their jointly trained policies. We show that our approach outperforms approaches without adaptive capabilities in a novel cooperative digit-prediction environment and discuss its limitations in the traffic junction environment.

Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman

In combination with Reinforcement Learning, Monte-Carlo Tree Search has shown to outperform human grandmasters in games such as Chess, Shogi and Go with little to no prior domain knowledge. However, most classical use cases only feature up to two players. Scaling the search to an arbitrary number of players presents a computational challenge, especially if decisions have to be planned over a longer time horizon. In this work, we investigate techniques that transform general-sum multiplayer games into single-player and two-player games that consider other agents to act according to given opponent models. For our evaluation, we focus on the challenging Pommerman environment which involves partial observability, a long time horizon and sparse rewards. In combination with our search methods, we investigate the phenomena of opponent modeling using heuristics and self-play. Overall, we demonstrate the effectiveness of our multiplayer search variants both in a supervised learning and reinforcement learning setting.