CAREER: Foundations of Dynamic Multi-Agent Learning Under Information Constraints — NSF Award to University of Maryland, College P
Recent years have witnessed significant progress of learning in dynamic environments. Many such success stories, e.g., AlphaGo, autonomous driving, and robot learning, naturally involve “multiple agents”. Note that these agents usually operate under “Information Constraints” as the underlying system state is only parti
| Award title | CAREER: Foundations of Dynamic Multi-Agent Learning Under Information Constraints |
|---|---|
| Award ID | 2443704 |
| Awardee | University of Maryland, College Park |
| City | COLLEGE PARK |
| State | MD |
| Amount obligated | $538,000 |
| Principal investigator | Kaiqing Zhang |
| Program | EPCN-Energy-Power-Ctrl-Netwrks |
| Start date | 02/15/2025 |
| Abstract | Recent years have witnessed significant progress of learning in dynamic environments. Many such success stories, e.g., AlphaGo, autonomous driving, and robot learning, naturally involve “multiple agents”. Note that these agents usually operate under “Information Constraints” as the underlying system state is only partially observable, and each agent only has local information that differs across agents. Despite the practical relevance, theoretical foundations for such settings are not well devel |
| Source | NSF Awards |
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