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A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Samuel Sokota · Ryan D'Orazio · J. Zico Kolter · Nicolas Loizou · Marc Lanctot · Ioannis Mitliagkas · Noam Brown · Christian Kroer
Event URL: https://openreview.net/forum?id=ndZ42T8iUmd »

Algorithms designed for single-agent reinforcement learning (RL) generally fail to converge to equilibria in two-player zero-sum (2p0s) games. On the other hand, game-theoretic algorithms for approximating Nash and regularized equilibria in 2p0s games are not typically competitive for RL and can be difficult to scale. As a result, algorithms for these two cases are generally developed and evaluated separately. In this work, we show that a single algorithm---a simple extension to mirror descent with proximal regularization that we call magnetic mirror descent (MMD)---can produce strong results in both settings, despite their fundamental differences. From a theoretical standpoint, we prove that MMD converges linearly to quantal response equilibria (i.e., entropy regularized Nash equilibria) in extensive-form games---this is the first time linear convergence has been proven for a first order solver. Moreover, applied as a tabular Nash equilibrium solver via self-play, we show empirically that MMD produces results competitive with CFR in both normal-form and extensive-form games---this is the first time that a standard RL algorithm has done so. Furthermore, for single-agent deep RL, on a small collection of Atari and Mujoco tasks, we show that MMD can produce results competitive with those of PPO. Lastly, for multi-agent deep RL, we show MMD can outperform NFSP in 3x3 Abrupt Dark Hex.

Author Information

Samuel Sokota (Carnegie Mellon University)
Ryan D'Orazio (Université de Montréal)
J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Zico Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, and also serves as Chief Scientist of AI Research for the Bosch Center for Artificial Intelligence. His work focuses on the intersection of machine learning and optimization, with a large focus on developing more robust, explainable, and rigorous methods in deep learning. In addition, he has worked on a number of application areas, highlighted by work on sustainability and smart energy systems. He is the recipient of the DARPA Young Faculty Award, and best paper awards at KDD, IJCAI, and PESGM.

Nicolas Loizou (Johns Hopkins University)
Marc Lanctot (DeepMind)
Ioannis Mitliagkas (University of Montreal)
Noam Brown (FAIR)
Christian Kroer (Columbia University)

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