A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov Games
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Abstract (arXiv)
An interesting iterative procedure is proposed to solve a two-player zero-sum Markov games. Under suitable assumption, the boundedness of the proposed iterates is obtained theoretically. Using results from stochastic approximation, the almost sure convergence of the proposed two-step minimax Q-learning is obtained theoretically. More specifically, the proposed algorithm converges to the game theoretic optimal value with probability one, when the model information is not known. Numerical simulation authenticate that the proposed algorithm is effective and easy to implement.
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References · 12
- 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 774
- 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
- 2016Safe and Efficient Off-Policy Reinforcement Learningcited by 260
- 2016Q(λ) with Off-Policy Correctionscited by 48
- 2017Multi-step Reinforcement Learning: A Unifying Algorithmcited by 42
- 2021Revisiting Peng's Q(λ) for Modern Reinforcement Learningcited by 19
- 2015True Online Temporal-Difference Learningcited by 16
- 2021A Discrete-Time Switching System Analysis of Q-learningcited by 16
- 2019A Generalized Minimax Q-learning Algorithm for Two-Player Zero-Sum Stochastic Gamescited by 7
- 2019Successive Over Relaxation Q-Learningcited by 4
- 2023Finite-Time Analysis of Minimax Q-Learning for Two-Player Zero-Sum Markov Games: Switching System Approachcited by 3
- 2024Two-Step Q-Learningcited by 1
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