Cover Image

Robust Adversarial Reinforcement Learning in Stochastic Games via Sequence Modeling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tang, Xiaohang, Cheng, Zhuowen, Kumar, Satyabrat
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computer Science and Game Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games
by: Altabaa, Awni, et al.
Published: (2023)

Learning the Expected Core of Strictly Convex Stochastic Cooperative Games
by: Tran, Nam Phuong, et al.
Published: (2024)

Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games
by: Yongacoglu, Bora, et al.
Published: (2021)

Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs
by: An, Zijian, et al.
Published: (2025)

Balancing of competitive two-player Game Levels with Reinforcement Learning
by: Rupp, Florian, et al.
Published: (2023)

Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness
by: Ehrenberg, Maayan, et al.
Published: (2024)

Last-Iterate Convergence of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
by: Chen, Zaiwei, et al.
Published: (2024)

A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent
by: Jafarnia-Jahromi, Mehdi, et al.
Published: (2021)

Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms
by: Nguyen-Tang, Thanh, et al.
Published: (2024)

Partially Observable Stochastic Games with Neural Perception Mechanisms
by: Yan, Rui, et al.
Published: (2023)

Prior-Agnostic Robust Forecast Aggregation
by: Chen, Zhi, et al.
Published: (2026)

Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equations
by: Thöni, Anna C. M., et al.
Published: (2025)

Provable Policy Gradient Methods for Average-Reward Markov Potential Games
by: Cheng, Min, et al.
Published: (2024)

RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning
by: Li, Boning, et al.
Published: (2024)

Copyright-Aware Incentive Scheme for Generative Art Models Using Hierarchical Reinforcement Learning
by: Shi, Zhuan, et al.
Published: (2024)

Actor-Dual-Critic Dynamics for Zero-sum and Identical-Interest Stochastic Games
by: Donmez, Ahmed Said, et al.
Published: (2026)

Approximating Nash Equilibria in General-Sum Games via Meta-Learning
by: Sychrovský, David, et al.
Published: (2025)

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

Strategically-Robust Learning Algorithms for Bidding in First-Price Auctions
by: Kumar, Rachitesh, et al.
Published: (2024)

Pessimism-Free Offline Learning in General-Sum Games via KL Regularization
by: Chen, Claire, et al.
Published: (2026)

Learning in Structured Stackelberg Games
by: Balcan, Maria-Florina, et al.
Published: (2025)

Learning to Steer Learners in Games
by: Zhang, Yizhou, et al.
Published: (2025)

Corrupted Learning Dynamics in Games
by: Tsuchiya, Taira, et al.
Published: (2024)

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

Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
by: Chen, Yang, et al.
Published: (2025)

Game Theory for Adversarial Attacks and Defenses
by: Sharma, Shorya
Published: (2021)

Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models
by: Aung, Aye Phyu Phyu, et al.
Published: (2024)

Regulation Games for Trustworthy Machine Learning
by: Yaghini, Mohammad, et al.
Published: (2024)

Nash Equilibria via Stochastic Eigendecomposition
by: Gemp, Ian
Published: (2024)

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

Adaptively Perturbed Mirror Descent for Learning in Games
by: Abe, Kenshi, et al.
Published: (2023)

Incentivized Learning in Principal-Agent Bandit Games
by: Scheid, Antoine, et al.
Published: (2024)

Learning-Augmented Online Bidding in Stochastic Settings
by: Angelopoulos, Spyros, et al.
Published: (2025)

Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games
by: Li, Tao, et al.
Published: (2024)

Learning from Random Demonstrations: Offline Reinforcement Learning with Importance-Sampled Diffusion Models
by: Fang, Zeyu, et al.
Published: (2024)

Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games
by: Hu, Junkai, et al.
Published: (2025)

Reinforcement Learning for Finite Space Mean-Field Type Games
by: Shao, Kai, et al.
Published: (2024)

Impact of Decentralized Learning on Player Utilities in Stackelberg Games
by: Donahue, Kate, et al.
Published: (2024)

Beyond Pessimism: Offline Learning in KL-regularized Games
by: Zhang, Yuheng, et al.
Published: (2026)

Learning in Bayesian Stackelberg Games With Unknown Follower's Types
by: Bollini, Matteo, et al.
Published: (2026)