Imagen de Portada

A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov Games

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	R, Shreyas S, Vijesh, Antony
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Two-Step Q-Learning
por: Vijesh, Antony, et al.
Publicado: (2024)

Corruption-Robust Offline Two-Player Zero-Sum Markov Games
por: Nika, Andi, et al.
Publicado: (2024)

Offline Two-Player Zero-Sum Markov Games with KL Regularization
por: Chen, Claire, et al.
Publicado: (2026)

Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
por: Park, Chanwoo, et al.
Publicado: (2023)

Deep SOR Minimax Q-learning for Two-player Zero-sum Game
por: Gautam, Saksham, et al.
Publicado: (2025)

Two-Player Zero-Sum Games with Bandit Feedback
por: Yılmaz, Elif, et al.
Publicado: (2025)

Tight Regret Upper and Lower Bounds for Optimistic Hedge in Two-Player Zero-Sum Games
por: Tsuchiya, Taira
Publicado: (2025)

A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games
por: Agarwal, Shubhankar, et al.
Publicado: (2025)

Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games
por: Feng, Songtao, et al.
Publicado: (2023)

Solving Zero-Sum Convex Markov Games
por: Kalogiannis, Fivos, et al.
Publicado: (2025)

Minimax-Optimal Policy Regret in Partially Observable Markov Games
por: Arora, Raman
Publicado: (2026)

Value Approximation for Two-Player General-Sum Differential Games with State Constraints
por: Zhang, Lei, et al.
Publicado: (2023)

Bilevel Optimization over Saddle Points of Zero-Sum Markov Games
por: Zheng, Zihao, et al.
Publicado: (2026)

Policy Iteration for Two-Player General-Sum Stochastic Stackelberg Games
por: Kudo, Mikoto, et al.
Publicado: (2024)

Achieving Logarithmic Regret in KL-Regularized Zero-Sum Markov Games
por: Nayak, Anupam, et al.
Publicado: (2025)

Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games
por: Gao, Minbo, et al.
Publicado: (2023)

Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games
por: Yan, Yuling, et al.
Publicado: (2022)

Learning in Zero-Sum Markov Games: Relaxing Strong Reachability and Mixing Time Assumptions
por: Ouhamma, Reda, et al.
Publicado: (2023)

Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback
por: Ito, Shinji, et al.
Publicado: (2025)

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
por: Afreen Islam, et al.
Publicado: (2025)

Large Language Models as Agents in Two-Player Games
por: Liu, Yang, et al.
Publicado: (2024)

Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning
por: R, Shreyas S
Publicado: (2024)

Understanding Dynamics of Adam in Zero-Sum Games: An ODE Approach
por: Feng, Yi, et al.
Publicado: (2026)

Two-Player Zero-Sum Hybrid Games
por: Leudo, Santiago J., et al.
Publicado: (2024)

Convergence of Time-Averaged Mean Field Gradient Descent Dynamics for Continuous Multi-Player Zero-Sum Games
por: Lu, Yulong, et al.
Publicado: (2025)

Bayesian Learning in Episodic Zero-Sum Games
por: Yueh, Chang-Wei, et al.
Publicado: (2026)

Software Fairness Dilemma: Is Bias Mitigation a Zero-Sum Game?
por: Chen, Zhenpeng, et al.
Publicado: (2025)

Unregularized Linear Convergence in Zero-Sum Game from Preference Feedback
por: Chen, Shulun, et al.
Publicado: (2025)

Minimax Optimal Q Learning with Nearest Neighbors
por: Zhao, Puning, et al.
Publicado: (2023)

Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games
por: Saadat, Kimiya, et al.
Publicado: (2024)

Minimax Optimal Two-Stage Algorithm For Moment Estimation Under Covariate Shift
por: Zhang, Zhen, et al.
Publicado: (2025)

Leveraging Noisy Observations in Zero-Sum Games
por: Athanasakos, Emmanouil M, et al.
Publicado: (2024)

Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy Optimization
por: Bozkus, Talha, et al.
Publicado: (2024)

Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty
por: Neufeld, Ariel, et al.
Publicado: (2022)

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
por: Lin, Tianyi, et al.
Publicado: (2024)

Adversarial Training Should Be Cast as a Non-Zero-Sum Game
por: Robey, Alexander, et al.
Publicado: (2023)

Finite-Time Analysis of Q-Value Iteration for General-Sum Stackelberg Games
por: Jeong, Narim, et al.
Publicado: (2026)

Optimism Without Regularization: Constant Regret in Zero-Sum Games
por: Lazarsfeld, John, et al.
Publicado: (2025)

Rapid Learning in Constrained Minimax Games with Negative Momentum
por: Fang, Zijian, et al.
Publicado: (2024)

Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players
por: Alatur, Pragnya, et al.
Publicado: (2024)