{"total":89,"rows":[{"id":"2110.14555","title":"V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL","created":"2021-10-27","cited_by_count":114,"area":null},{"id":"2111.04850","title":"Dueling RL: Reinforcement Learning with Trajectory Preferences","created":"2021-11-08","cited_by_count":106,"area":null},{"id":"2101.04233","title":"Independent Policy Gradient Methods for Competitive Reinforcement Learning","created":"2021-01-11","cited_by_count":101,"area":null},{"id":"2102.04540","title":"Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games","created":"2021-02-08","cited_by_count":69,"area":null},{"id":"2110.04184","title":"When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?","created":"2021-10-08","cited_by_count":67,"area":null},{"id":"2106.02748","title":"Decentralized Q-Learning in Zero-sum Markov Games","created":"2021-06-04","cited_by_count":58,"area":null},{"id":"2204.03991","title":"The Complexity of Markov Equilibrium in Stochastic Games","created":"2022-04-08","cited_by_count":49,"area":null},{"id":"2106.03352","title":"The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces","created":"2021-06-07","cited_by_count":48,"area":null},{"id":"2111.12306","title":"Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability","created":"2021-11-24","cited_by_count":44,"area":null},{"id":"2202.07511","title":"Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets","created":"2022-02-15","cited_by_count":44,"area":null},{"id":"2107.14702","title":"Towards General Function Approximation in Zero-Sum Markov Games","created":"2021-07-30","cited_by_count":43,"area":null},{"id":"2110.05682","title":"Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games","created":"2021-10-12","cited_by_count":42,"area":null},{"id":"2102.11494","title":"Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games","created":"2021-02-23","cited_by_count":40,"area":null},{"id":"2110.05707","title":"On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning","created":"2021-10-12","cited_by_count":36,"area":null},{"id":"2302.06606","title":"Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation","created":"2023-02-13","cited_by_count":30,"area":null},{"id":"2303.02738","title":"Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback","created":"2023-03-05","cited_by_count":30,"area":null},{"id":"2201.03522","title":"When is Offline Two-Player Zero-Sum Markov Game Solvable?","created":"2022-01-10","cited_by_count":28,"area":null},{"id":"2206.01315","title":"Sample-Efficient Reinforcement Learning of Partially Observable Markov Games","created":"2022-06-02","cited_by_count":27,"area":null},{"id":"2302.03673","title":"Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation","created":"2023-02-07","cited_by_count":27,"area":null},{"id":"2202.01752","title":"Near-Optimal Learning of Extensive-Form Games with Imperfect Information","created":"2022-02-03","cited_by_count":26,"area":null},{"id":"2210.01050","title":"Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games","created":"2022-10-03","cited_by_count":26,"area":null},{"id":"2102.07404","title":"Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games","created":"2021-02-15","cited_by_count":25,"area":null},{"id":"2112.13521","title":"Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?","created":"2021-12-27","cited_by_count":24,"area":null},{"id":"2207.14211","title":"Regret Minimization and Convergence to Equilibria in General-sum Markov Games","created":"2022-07-28","cited_by_count":24,"area":null},{"id":"2102.03192","title":"Provably Efficient Algorithms for Multi-Objective Competitive RL","created":"2021-02-05","cited_by_count":22,"area":null},{"id":"2111.11743","title":"Independent Learning in Stochastic Games","created":"2021-11-23","cited_by_count":22,"area":null},{"id":"2205.14590","title":"Independent and Decentralized Learning in Markov Potential Games","created":"2022-05-29","cited_by_count":22,"area":null},{"id":"2106.06279","title":"Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall","created":"2021-06-11","cited_by_count":21,"area":null},{"id":"2305.18258","title":"Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration","created":"2023-05-29","cited_by_count":20,"area":null},{"id":"2102.08903","title":"Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games","created":"2021-02-17","cited_by_count":18,"area":null},{"id":"2203.06803","title":"Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits","created":"2022-03-14","cited_by_count":17,"area":null},{"id":"2206.02640","title":"Policy Optimization for Markov Games: Unified Framework and Faster Convergence","created":"2022-06-06","cited_by_count":16,"area":null},{"id":"2210.01907","title":"A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games","created":"2022-10-04","cited_by_count":16,"area":null},{"id":"2208.10458","title":"Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model","created":"2022-08-22","cited_by_count":15,"area":null},{"id":"2210.16976","title":"Representation Learning for General-sum Low-rank Markov Games","created":"2022-10-30","cited_by_count":15,"area":null},{"id":"2303.03100","title":"A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games","created":"2023-03-03","cited_by_count":15,"area":null},{"id":"2203.07322","title":"Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation","created":"2022-03-14","cited_by_count":14,"area":null},{"id":"2305.11283","title":"On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation","created":"2023-05-18","cited_by_count":14,"area":null},{"id":"2306.00212","title":"Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning","created":"2023-05-31","cited_by_count":14,"area":null},{"id":"2401.15240","title":"Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games","created":"2024-01-26","cited_by_count":13,"area":null},{"id":"2206.01588","title":"Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games","created":"2022-06-03","cited_by_count":12,"area":null},{"id":"2209.12430","title":"$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games","created":"2022-09-26","cited_by_count":11,"area":null},{"id":"2212.12567","title":"Adapting to game trees in zero-sum imperfect information games","created":"2022-12-23","cited_by_count":11,"area":null},{"id":"2210.09257","title":"Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers","created":"2022-10-17","cited_by_count":10,"area":null},{"id":"2303.12287","title":"Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games","created":"2023-03-22","cited_by_count":10,"area":null},{"id":"2307.09470","title":"Multi-Player Zero-Sum Markov Games with Networked Separable Interactions","created":"2023-07-13","cited_by_count":10,"area":null},{"id":"2110.04638","title":"Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games","created":"2021-10-09","cited_by_count":9,"area":null},{"id":"2207.12463","title":"Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions","created":"2022-07-25","cited_by_count":9,"area":null},{"id":"2303.03095","title":"Can We Find Nash Equilibria at a Linear Rate in Markov Games?","created":"2023-03-03","cited_by_count":9,"area":null},{"id":"2105.06784","title":"Efficient PAC Reinforcement Learning in Regular Decision Processes","created":"2021-05-14","cited_by_count":7,"area":null},{"id":"2302.01477","title":"A Reduction-based Framework for Sequential Decision Making with Delayed Feedback","created":"2023-02-03","cited_by_count":7,"area":null},{"id":"2406.06823","title":"Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies","created":"2024-06-10","cited_by_count":6,"area":null},{"id":"2605.13025","title":"Offline Two-Player Zero-Sum Markov Games with KL Regularization","created":"2026-05-13","cited_by_count":6,"area":null},{"id":"2107.00685","title":"Gap-Dependent Bounds for Two-Player Markov Games","created":"2021-07-01","cited_by_count":5,"area":null},{"id":"2202.06694","title":"Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences","created":"2022-02-14","cited_by_count":5,"area":null},{"id":"2505.15251","title":"Loss-Guided Auxiliary Agents for Overcoming Mode Collapse in GFlowNets","created":"2025-05-21","cited_by_count":5,"area":null},{"id":"2108.07472","title":"Is Nash Equilibrium Approximator Learnable?","created":"2021-08-17","cited_by_count":4,"area":null},{"id":"2109.03396","title":"A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent","created":"2021-09-08","cited_by_count":4,"area":null},{"id":"2112.07859","title":"Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games","created":"2021-12-15","cited_by_count":4,"area":null},{"id":"2201.13170","title":"Cooperative Online Learning in Stochastic and Adversarial MDPs","created":"2022-01-31","cited_by_count":4,"area":null},{"id":"2303.00177","title":"Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation","created":"2023-03-01","cited_by_count":4,"area":null},{"id":"2107.14698","title":"Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning","created":"2021-07-30","cited_by_count":3,"area":null},{"id":"2207.08894","title":"A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games","created":"2022-07-18","cited_by_count":3,"area":null},{"id":"2302.03364","title":"Population-size-Aware Policy Optimization for Mean-Field Games","created":"2023-02-07","cited_by_count":3,"area":null},{"id":"2306.07465","title":"A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning","created":"2023-06-12","cited_by_count":3,"area":null},{"id":"2411.00707","title":"Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms","created":"2024-11-01","cited_by_count":3,"area":null},{"id":"2411.01166","title":"Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions","created":"2024-11-02","cited_by_count":3,"area":null},{"id":"2309.00656","title":"Local and adaptive mirror descents in extensive-form games","created":"2023-09-01","cited_by_count":2,"area":null},{"id":"2310.04796","title":"Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning","created":"2023-10-07","cited_by_count":2,"area":null},{"id":"2404.19292","title":"Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning","created":"2024-04-30","cited_by_count":2,"area":null},{"id":"2407.10207","title":"Learning to Steer Markovian Agents under Model Uncertainty","created":"2024-07-14","cited_by_count":2,"area":null},{"id":"2603.07705","title":"Deep Incentive Design with Differentiable Equilibrium Blocks","created":"2026-03-08","cited_by_count":2,"area":null},{"id":"2208.05363","title":"Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium","created":"2022-08-10","cited_by_count":1,"area":null},{"id":"2211.15792","title":"Provably Efficient Model-free RL in Leader-Follower MDP with Linear Function Approximation","created":"2022-11-28","cited_by_count":1,"area":null},{"id":"2310.06243","title":"Sample-Efficient Multi-Agent RL: An Optimization Perspective","created":"2023-10-10","cited_by_count":1,"area":null},{"id":"2403.15045","title":"DP-Dueling: Learning from Preference Feedback without Compromising User Privacy","created":"2024-03-22","cited_by_count":1,"area":null},{"id":"2410.09701","title":"Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models","created":"2024-10-13","cited_by_count":1,"area":null},{"id":"2511.02157","title":"Near Optimal Convergence to Coarse Correlated Equilibrium in General-Sum Markov Games","created":"2025-11-04","cited_by_count":1,"area":null},{"id":"2602.07205","title":"Online Learning for Uninformed Markov Games: Empirical Nash-Value Regret and Non-Stationarity Adaptation","created":"2026-02-06","cited_by_count":1,"area":null},{"id":"2210.11402","title":"Learning Rationalizable Equilibria in Multiplayer Games","created":"2022-10-20","cited_by_count":0,"area":null},{"id":"2301.05630","title":"Decentralized model-free reinforcement learning in stochastic games with average-reward objective","created":"2023-01-13","cited_by_count":0,"area":null},{"id":"2308.08858","title":"Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games","created":"2023-08-17","cited_by_count":0,"area":null},{"id":"2403.07890","title":"$\\widetilde{O}(T^{-1})$ Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games","created":"2024-02-02","cited_by_count":0,"area":null},{"id":"2403.11544","title":"RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model","created":"2024-03-18","cited_by_count":0,"area":null},{"id":"2405.02724","title":"Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning","created":"2024-05-04","cited_by_count":0,"area":null},{"id":"2405.16628","title":"Competing for pixels: a self-play algorithm for weakly-supervised segmentation","created":"2024-05-26","cited_by_count":0,"area":null},{"id":"2509.00179","title":"Playing Markov Games Without Observing Payoffs","created":"2025-08-29","cited_by_count":0,"area":null},{"id":"2512.01245","title":"Bayesian Optimization for Non-Cooperative Game-Based Radio Resource Management","created":"2025-12-01","cited_by_count":0,"area":null},{"id":"2610.05968","title":"Fast Last-Iterate Convergence in Zero-Sum Markov Games with Bandit Feedback","created":"2026-10-05","cited_by_count":0,"area":null}],"next_cursor":null}