Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization

Author:

Cen Shicong1,Chen Fan2,Chi Yuejie1

Affiliation:

1. Carnegie Mellon University,Department of Electrical and Computer Engineering

2. Peking University,Department of Mathematics

Publisher

IEEE

Reference54 articles.

1. Independent policy gradient methods for competitive reinforcement learning;daskalakis;Advances in neural information processing systems,2020

2. Near-optimal no-regret learning in general games;daskalakis;Advances in neural information processing systems,2021

3. Global convergence of policy gradient methods for the linear quadratic regulator;fazel;International Conference on Machine Learning,2018

4. Independent policy gradient for large-scale markov potential games: Sharper rates, function approximation, and game-agnostic convergence;ding,2022

5. Gradient play in stochastic games: stationary points, convergence, and sample complexity;zhang,2021

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