#  Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games 

 



####  calendar\_today Date and Time 

 **March 4, 2024** 

 01:30PM - 02:30PM EST 

####  pin\_drop Location 

  **SEC 2.122 + 2.123 (second floor).**  



 

 



 

 **Speaker:** Brian Zhang (CMU)

**Title:** Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games **Abstract:** We introduce a unified framework for understanding *optimal equilibria* in general games by explicitly augmenting the game with a *mediator* who can communicate with players. By varying restrictions governing the communication, the framework covers a wide family of solution concepts and problems including---but not limited to---communication equilibria, mechanism and information design, and extensive-form correlated equilibria. We will then demonstrate a reduction from computing optimal equilibria in this framework to solving *zero-sum games*. This reformulation allows us to apply the vast family of techniques for learning in zero-sum games, yielding the first learning algorithms for optimal equilibria in general games. Within this framework, we show that optimal extensive-form correlated equilibria---which are NP-hard to compute---correspond to the zero-sum game having *imperfect recall*, and that this imperfect recall is precisely the reason for the hardness of computation. We demonstrate the practical scalability and flexibility of our approach by attaining state-of-the-art performance in benchmark tabular games, and by computing an optimal mechanism for a sequential auction design problem using deep reinforcement learning.



 

 



 

 See also:- [ Spring 2024 EconCS Seminars ](/taxonomycalendarseminar/seminars-2024-spring)
- [ Seminar history ](/taxonomycalendarseminar/seminar-history)
 
 

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