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Poster
When are Offline Two-Player Zero-Sum Markov Games Solvable?
Qiwen Cui · Simon Du

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #740
in Poster Session 6 »

We study what dataset assumption permits solving offline two-player zero-sum Markov games. In stark contrast to the offline single-agent Markov decision process, we show that the single strategy concentration assumption is insufficient for learning the Nash equilibrium (NE) strategy in offline two-player zero-sum Markov games. On the other hand, we propose a new assumption named unilateral concentration and design a pessimism-type algorithm that is provably efficient under this assumption. In addition, we show that the unilateral concentration assumption is necessary for learning an NE strategy. Furthermore, our algorithm can achieve minimax sample complexity without any modification for two widely studied settings: dataset with uniform concentration assumption and turn-based Markov games. Our work serves as an important initial step towards understanding offline multi-agent reinforcement learning.

Keywords: offline reinforcement learning · Reinforcement Learning Theory · zero-sum Markov game

Author Information

Qiwen Cui (Department of Computer Science, University of Washington)
Simon Du (University of Washington)

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