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Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination. In some settings, the structure of a problem allows a distributed solution with limited communication. Here, we consider a scenario where no communication is available, and instead we learn local policies for all agents that collectively mimic the solution to a centralized multi-agent static optimization problem. Our main contribution is an information theoretic framework based on rate distortion theory which facilitates analysis of how well the resulting fully decentralized policies are able to reconstruct the optimal solution. Moreover, this framework provides a natural extension that addresses which nodes an agent should communicate with to improve the performance of its individual policy.
Author Information
Roel Dobbe (AI Now Institute, New York University)
Roel’s research addresses the development, analysis, integration and governance of data-driven systems. His PhD work combined optimization, machine learning and control theory to enable monitoring and control of safety-critical systems, including energy & power systems and cancer diagnosis and treatment. In addition to research, Roel has experience in industry and public institutions, where he has served as a management consultant for AT Kearney, a data scientist for C3 IoT, and a researcher for the National ThinkTank in The Netherlands. His diverse background led him to examine the ways in which values and stakeholder perspectives are represented in the process of designing and deploying AI and algorithmic decision-making and control systems. Roel is passionate about developing practices to help engineers and computer scientists engage more closely both with impacted communities and scholars in the social sciences, and to better contend with serious questions of ethics and governance. Towards this end, Roel founded Graduates for Engaged and Extended Scholarship around Computing & Engineering (GEESE); a student organization stimulating graduate students across all disciplines studying or developing technologies to take a broader lens at their field of study and engage across disciplines. Roel has published his work in various journals and conferences, including Automatica, the IEEE Conference on Decision and Control, the IEEE Power & Energy Society General Meeting, IEEE/ACM Transactions on Computational Biology and Bioinformatics and NeurIPS.
David Fridovich-Keil (UC Berkeley)
Claire Tomlin (UC Berkeley)
More from the Same Authors
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2017 Poster: Countering Feedback Delays in Multi-Agent Learning »
Zhengyuan Zhou · Panayotis Mertikopoulos · Nicholas Bambos · Peter W Glynn · Claire Tomlin -
2016 Poster: Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games »
Maximilian Balandat · Walid Krichene · Claire Tomlin · Alexandre Bayen