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Communicating via Markov Decision Processes
Samuel Sokota · Christian Schroeder · Maximilian Igl · Luisa Zintgraf · Phil Torr · Martin Strohmeier · Zico Kolter · Shimon Whiteson · Jakob Foerster

Wed Jul 20 10:30 AM -- 10:35 AM (PDT) @ Room 307
in Reinforcement Learning »

We consider the problem of communicating exogenous information by means of Markov decision process trajectories. This setting, which we call a Markov coding game (MCG), generalizes both source coding and a large class of referential games. MCGs also isolate a problem that is important in decentralized control settings in which cheap-talk is not available---namely, they require balancing communication with the associated cost of communicating. We contribute a theoretically grounded approach to MCGs based on maximum entropy reinforcement learning and minimum entropy coupling that we call MEME. Due to recent breakthroughs in approximation algorithms for minimum entropy coupling, MEME is not merely a theoretical algorithm, but can be applied to practical settings. Empirically, we show both that MEME is able to outperform a strong baseline on small MCGs and that MEME is able to achieve strong performance on extremely large MCGs. To the latter point, we demonstrate that MEME is able to losslessly communicate binary images via trajectories of Cartpole and Pong, while simultaneously achieving the maximal or near maximal expected returns, and that it is even capable of performing well in the presence of actuator noise.

Keywords: RL: Everything Else · RL: Multi-agent · MISC: Everything Else

Author Information

Samuel Sokota (Carnegie Mellon University)
Christian Schroeder (University of Oxford)
Maximilian Igl (University of Oxford)
Luisa Zintgraf (University of Oxford)
Phil Torr (Oxford)
Martin Strohmeier (armasuisse Science + Technology)
Zico Kolter (Carnegie Mellon University / Bosch Center for AI)
Shimon Whiteson (University of Oxford)
Jakob Foerster (Oxford university)
Jakob Foerster

Jakob Foerster started as an Associate Professor at the department of engineering science at the University of Oxford in the fall of 2021. During his PhD at Oxford he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI, and DeepMind. After his PhD he worked as a research scientist at Facebook AI Research in California, where he continued doing foundational work. He was the lead organizer of the first Emergent Communication workshop at NeurIPS in 2017, which he has helped organize ever since and was awarded a prestigious CIFAR AI chair in 2019. His past work addresses how AI agents can learn to cooperate and communicate with other agents, most recently he has been developing and addressing the zero-shot coordination problem setting, a crucial step towards human-AI coordination.

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