DRAFT
Caltech Logo

H.B. Keller Colloquium

Monday, April 26, 2021
4:00pm to 5:00pm
Add to Cal
Online Event
Reinforcement Learning for Complex Environments: Tree Search, Function Approximators and Markov Games
Qiaomin Xie, Visiting Assistant Professor at Cornell, School of Operations Research and Information Engineering, Cornell University,

Recent literature has witnessed much progress on the algorithmic and theoretical foundations of Reinforcement Learning (RL), particularly for single-agent problems with small state/action spaces. Our understanding and algorithm toolbox for RL under complex environments, however, remain relatively limited. In this talk, I will discuss some of my work on scalable and probably efficient RL for the challenging settings with large spaces and multiple strategic agents. 

First, I will focus on simulation-based methods, as exemplified by Monte-Carlo Tree Search (MCTS). MCTS is a powerful paradigm for online planning that enjoys remarkable empirical success, but lacks theoretical understanding. We provide a complete and rigorous non-asymptotic analysis of MCTS. Our analysis develops a general framework based on a hierarchy of bandits, and highlights the importance of using a non-standard confidence bound (also used by AlphaGo) for convergence. I will further discuss combining MCTS with supervised learning and its generalization to continuous action space. 

In the second part of the talk, I will discuss on-policy RL for zero-sum Markov games, which generalizes Markov decision processes to multi-agent settings. We consider function approximation to deal with continuous and unbounded state spaces. Based on a fruitful marriage with algorithmic game theory, we develop the first computational efficient algorithm for this setting, with a provable regret bound that is independent of the cardinality and ambient dimension of the state space.

For more information, please contact Diana Bohler by phone at 626-232-6138 or by email at [email protected].