TOWARDS GENERAL FUNCTION APPROXIMATION IN ZERO-SUM MARKOV GAMES

Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang

Research output: Contribution to conferencePaperpeer-review

7 Scopus citations

Abstract

This paper considers two-player zero-sum finite-horizon Markov games with simultaneous moves. The study focuses on the challenging settings where the value function or the model is parameterized by general function classes. Provably efficient algorithms for both decoupled and coordinated settings are developed. In the decoupled setting where the agent controls a single player and plays against an arbitrary opponent, we propose a new model-free algorithm. The sample complexity is governed by the Minimax Eluder dimension-a new dimension of the function class in Markov games. As a special case, this method improves the state-of-the-art algorithm by a d factor in the regret when the reward function and transition kernel are parameterized with d-dimensional linear features. In the coordinated setting where both players are controlled by the agent, we propose a model-based algorithm and a model-free algorithm. In the model-based algorithm, we prove that sample complexity can be bounded by a generalization of Witness rank to Markov games. The model-free algorithm enjoys a K-regret upper bound where K is the number of episodes.

Original languageEnglish (US)
StatePublished - 2022
Externally publishedYes
Event10th International Conference on Learning Representations, ICLR 2022 - Virtual, Online
Duration: Apr 25 2022Apr 29 2022

Conference

Conference10th International Conference on Learning Representations, ICLR 2022
CityVirtual, Online
Period4/25/224/29/22

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Computer Science Applications
  • Education
  • Linguistics and Language

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