A Regret Minimization Approach to Mutli-Agent Control and RL

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=DVbuDOmaN_k


Duration: 42:15
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Elad Hazan (Princeton University and Google Research)
https://simons.berkeley.edu/talks/regret-minimization-approach-mutli-agent-control-and-rl
Multi-Agent Reinforcement Learning and Bandit Learning

We'll start by describing a new paradigm in reinforcement learning called nonstochastic control, how it relates to existing frameworks, and survey efficient gradient-based methods for regret minimization in this model. We then proceed to describe recent work on multi-agent learning based on regret minimization methods that reach an equilibrium. We'll conclude with remaining challenges and potential directions for further research.



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Multi-Agent Reinforcement Learning and Bandit Learning
Elad Hazan