{"total":34,"rows":[{"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":"2107.14702","title":"Towards General Function Approximation in Zero-Sum Markov Games","created":"2021-07-30","cited_by_count":43,"area":null},{"id":"2211.05732","title":"The Sample Complexity of Online Contract Design","created":"2022-11-10","cited_by_count":35,"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":"2207.00046","title":"Performative Reinforcement Learning","created":"2022-06-30","cited_by_count":27,"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":"2210.16976","title":"Representation Learning for General-sum Low-rank Markov Games","created":"2022-10-30","cited_by_count":15,"area":null},{"id":"2301.11518","title":"Online Learning in Stackelberg Games with an Omniscient Follower","created":"2023-01-27","cited_by_count":15,"area":null},{"id":"2111.00781","title":"Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure","created":"2021-11-01","cited_by_count":14,"area":null},{"id":"2210.11942","title":"Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning","created":"2022-10-19","cited_by_count":12,"area":null},{"id":"2402.04971","title":"Multi-Sender Persuasion: A Computational Perspective","created":"2024-02-07","cited_by_count":9,"area":null},{"id":"2312.10230","title":"Constrained Meta-Reinforcement Learning for Adaptable Safety Guarantee with Differentiable Convex Programming","created":"2023-12-15","cited_by_count":8,"area":null},{"id":"2309.09801","title":"Learning Optimal Contracts: How to Exploit Small Action Spaces","created":"2023-09-18","cited_by_count":5,"area":null},{"id":"2310.04884","title":"Regret Analysis of Repeated Delegated Choice","created":"2023-10-07","cited_by_count":5,"area":null},{"id":"2403.00188","title":"Impact of Decentralized Learning on Player Utilities in Stackelberg Games","created":"2024-02-29","cited_by_count":5,"area":null},{"id":"2210.12470","title":"Learning Correlated Stackelberg Equilibrium in General-Sum Multi-Leader-Single-Follower Games","created":"2022-10-22","cited_by_count":4,"area":null},{"id":"2207.07971","title":"A Survey of Decision Making in Adversarial Games","created":"2022-07-16","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":"2502.09780","title":"Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games","created":"2025-02-13","cited_by_count":3,"area":null},{"id":"2602.00771","title":"Learning in Bayesian Stackelberg Games With Unknown Follower's Types","created":"2026-01-31","cited_by_count":2,"area":null},{"id":"2603.03759","title":"Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling","created":"2026-03-04","cited_by_count":2,"area":null},{"id":"2111.05670","title":"DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning","created":"2021-11-10","cited_by_count":1,"area":null},{"id":"2201.01163","title":"Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning","created":"2022-01-03","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":"2310.02459","title":"Distributionally Safe Reinforcement Learning under Model Uncertainty: A Single-Level Approach by Differentiable Convex Programming","created":"2023-10-03","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":"2402.07437","title":"Learning Optimal Tax Design in Nonatomic Congestion Games","created":"2024-02-12","cited_by_count":1,"area":null},{"id":"2207.04054","title":"Online Learning in Supply-Chain Games","created":"2022-07-08","cited_by_count":0,"area":null},{"id":"2211.01703","title":"$2 \\times 2$ Zero-Sum Games with Commitments and Noisy Observations","created":"2022-11-03","cited_by_count":0,"area":null},{"id":"2301.11802","title":"Decentralized Online Bandit Optimization on Directed Graphs with Regret Bounds","created":"2023-01-27","cited_by_count":0,"area":null},{"id":"2302.01421","title":"Follower Agnostic Methods for Stackelberg Games","created":"2023-02-02","cited_by_count":0,"area":null},{"id":"2302.03438","title":"Uncoupled Learning of Differential Stackelberg Equilibria with Commitments","created":"2023-02-07","cited_by_count":0,"area":null},{"id":"2402.02861","title":"Leveraging Noisy Observations in Zero-Sum Games","created":"2024-02-05","cited_by_count":0,"area":null},{"id":"2405.03158","title":"Decentralized Online Learning in General-Sum Stackelberg Games","created":"2024-05-06","cited_by_count":0,"area":null}],"next_cursor":null}