{"total":103,"rows":[{"id":"2310.18940","title":"Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game","created":"2023-10-29","cited_by_count":152,"area":"agents"},{"id":"2309.17277","title":"Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4","created":"2023-09-29","cited_by_count":68,"area":"agents"},{"id":"2106.06135","title":"DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning","created":"2021-06-11","cited_by_count":66,"area":null},{"id":"2206.05825","title":"A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games","created":"2022-06-12","cited_by_count":66,"area":null},{"id":"2110.10614","title":"Independent Natural Policy Gradient Always Converges in Markov Potential Games","created":"2021-10-20","cited_by_count":37,"area":null},{"id":"2103.06426","title":"XDO: A Double Oracle Algorithm for Extensive-Form Games","created":"2021-03-11","cited_by_count":34,"area":null},{"id":"2310.19647","title":"Fast swap regret minimization and applications to approximate correlated equilibria","created":"2023-10-30","cited_by_count":32,"area":null},{"id":"2106.14326","title":"Last-iterate Convergence in Extensive-Form Games","created":"2021-06-27","cited_by_count":28,"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":"2408.01072","title":"A Survey on Self-play Methods in Reinforcement Learning","created":"2024-08-02","cited_by_count":26,"area":"deeprl"},{"id":"2206.04122","title":"ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret","created":"2022-06-08","cited_by_count":24,"area":null},{"id":"2112.03178","title":"Student of Games: A unified learning algorithm for both perfect and imperfect information games","created":"2021-12-06","cited_by_count":23,"area":null},{"id":"2206.09495","title":"The Power of Regularization in Solving Extensive-Form Games","created":"2022-06-19","cited_by_count":21,"area":null},{"id":"2401.06781","title":"PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model","created":"2024-01-04","cited_by_count":20,"area":null},{"id":"2310.00322","title":"Evolving Diverse Red-team Language Models in Multi-round Multi-agent Games","created":"2023-09-30","cited_by_count":17,"area":"agents"},{"id":"2102.06973","title":"Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games","created":"2021-02-13","cited_by_count":15,"area":null},{"id":"2203.16406","title":"PerfectDou: Dominating DouDizhu with Perfect Information Distillation","created":"2022-03-30","cited_by_count":15,"area":null},{"id":"2502.04686","title":"Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization","created":"2025-02-07","cited_by_count":15,"area":"agents"},{"id":"2207.06541","title":"Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games","created":"2022-07-13","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":"2303.05197","title":"Mastering Strategy Card Game (Hearthstone) with Improved Techniques","created":"2023-03-09","cited_by_count":10,"area":null},{"id":"2403.02227","title":"Policy Space Response Oracles: A Survey","created":"2024-03-04","cited_by_count":10,"area":null},{"id":"2403.04344","title":"RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning","created":"2024-03-07","cited_by_count":10,"area":"deeprl"},{"id":"2111.05884","title":"Search in Imperfect Information Games","created":"2021-11-10","cited_by_count":8,"area":null},{"id":"2106.00897","title":"Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play","created":"2021-06-02","cited_by_count":7,"area":null},{"id":"2106.04219","title":"Time-series Imputation of Temporally-occluded Multiagent Trajectories","created":"2021-06-08","cited_by_count":7,"area":null},{"id":"2110.04961","title":"Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent","created":"2021-10-11","cited_by_count":7,"area":null},{"id":"2110.14221","title":"Learning Diverse Policies in MOBA Games via Macro-Goals","created":"2021-10-27","cited_by_count":7,"area":null},{"id":"2305.17342","title":"Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL","created":"2023-05-27","cited_by_count":7,"area":null},{"id":"2404.13891","title":"Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent","created":"2024-04-22","cited_by_count":7,"area":null},{"id":"2310.03354","title":"Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games","created":"2023-10-05","cited_by_count":6,"area":null},{"id":"2312.02561","title":"DanZero+: Dominating the GuanDan Game through Reinforcement Learning","created":"2023-12-05","cited_by_count":6,"area":null},{"id":"2207.01773","title":"Approximating Discontinuous Nash Equilibrial Values of Two-Player General-Sum Differential Games","created":"2022-07-05","cited_by_count":5,"area":null},{"id":"2210.17087","title":"DanZero: Mastering GuanDan Game with Reinforcement Learning","created":"2022-10-31","cited_by_count":5,"area":null},{"id":"2408.00751","title":"A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergence","created":"2024-08-01","cited_by_count":5,"area":"deeprl"},{"id":"2105.08440","title":"CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Space","created":"2021-05-18","cited_by_count":4,"area":null},{"id":"2110.01810","title":"Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess","created":"2021-10-05","cited_by_count":4,"area":null},{"id":"2201.10377","title":"Public Information Representation for Adversarial Team Games","created":"2022-01-25","cited_by_count":4,"area":null},{"id":"2207.09597","title":"Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments","created":"2022-07-19","cited_by_count":4,"area":null},{"id":"2305.17327","title":"Hierarchical Deep Counterfactual Regret Minimization","created":"2023-05-27","cited_by_count":4,"area":null},{"id":"2406.02357","title":"The complexity of approximate (coarse) correlated equilibrium for incomplete information games","created":"2024-06-04","cited_by_count":4,"area":null},{"id":"2602.16928","title":"Discovering Multiagent Learning Algorithms with Large Language Models","created":"2026-02-18","cited_by_count":4,"area":null},{"id":"2607.28520","title":"Agents That Certify Their Own Exploits: Confidence-Scheduled Restricted Responses for Safe Opponent Exploitation","created":"2026-07-30","cited_by_count":4,"area":null},{"id":"2102.05026","title":"Multi-Agent Coordination in Adversarial Environments through Signal Mediated Strategies","created":"2021-02-09","cited_by_count":3,"area":null},{"id":"2104.12895","title":"Computational Performance of Deep Reinforcement Learning to find Nash Equilibria","created":"2021-04-26","cited_by_count":3,"area":null},{"id":"2110.15907","title":"Learning to Be Cautious","created":"2021-10-29","cited_by_count":3,"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":"2502.12436","title":"Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL","created":"2025-02-18","cited_by_count":3,"area":null},{"id":"2103.00187","title":"Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report","created":"2021-02-27","cited_by_count":2,"area":null},{"id":"2108.09444","title":"Temporal Induced Self-Play for Stochastic Bayesian Games","created":"2021-08-21","cited_by_count":2,"area":null},{"id":"2205.09056","title":"Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs","created":"2022-05-18","cited_by_count":2,"area":null},{"id":"2207.05285","title":"Offline Equilibrium Finding","created":"2022-07-12","cited_by_count":2,"area":"offlinerl"},{"id":"2303.01074","title":"Learning not to Regret","created":"2023-03-02","cited_by_count":2,"area":null},{"id":"2308.04719","title":"JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games","created":"2023-08-09","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":"2405.11669","title":"Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning","created":"2024-05-19","cited_by_count":2,"area":"deeprl"},{"id":"2510.05048","title":"Look-ahead Reasoning with a Learned Model in Imperfect Information Games","created":"2025-10-06","cited_by_count":2,"area":"lr"},{"id":"2510.18183","title":"NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria","created":"2025-10-21","cited_by_count":2,"area":"deeprl"},{"id":"2604.25796","title":"StratFormer: Adaptive Opponent Modeling and Exploitation in Imperfect-Information Games","created":"2026-04-28","cited_by_count":2,"area":null},{"id":"2605.14277","title":"Parallelizing Counterfactual Regret Minimization","created":"2026-05-14","cited_by_count":2,"area":null},{"id":"2608.06741","title":"Solver-Guided Reasoning for Mixed-Equilibrium Strategies","created":"2026-08-07","cited_by_count":2,"area":"lr"},{"id":"2105.12328","title":"D2CFR: Minimize Counterfactual Regret with Deep Dueling Neural Network","created":"2021-05-26","cited_by_count":1,"area":null},{"id":"2307.12087","title":"CFR-p: Counterfactual Regret Minimization with Hierarchical Policy Abstraction, and its Application to Two-player Mahjong","created":"2023-07-22","cited_by_count":1,"area":null},{"id":"2402.03136","title":"Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games","created":"2024-02-05","cited_by_count":1,"area":null},{"id":"2504.18868","title":"Approximating Nash Equilibria in General-Sum Games via Meta-Learning","created":"2025-04-26","cited_by_count":1,"area":null},{"id":"2506.01927","title":"Online Competitive Information Gathering for Partially Observable Trajectory Games","created":"2025-06-02","cited_by_count":1,"area":null},{"id":"2511.08174","title":"Deep (Predictive) Discounted Counterfactual Regret Minimization","created":"2025-11-11","cited_by_count":1,"area":null},{"id":"2605.09150","title":"AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Play","created":"2026-05-09","cited_by_count":1,"area":null},{"id":"2606.29457","title":"How Much Due Diligence Before You Bid? Learning in Intractable Takeover Auctions","created":"2026-06-28","cited_by_count":1,"area":null},{"id":"2608.06362","title":"AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games","created":"2026-08-06","cited_by_count":1,"area":"agents"},{"id":"2609.04303","title":"Abstraction Agent","created":"2026-09-03","cited_by_count":1,"area":"agents"},{"id":"2112.10890","title":"Revisiting Game Representations: The Hidden Costs of Efficiency in Sequential Decision-making Algorithms","created":"2021-12-20","cited_by_count":0,"area":null},{"id":"2205.12031","title":"Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections","created":"2022-05-24","cited_by_count":0,"area":null},{"id":"2209.07758","title":"Game-theoretic Objective Space Planning","created":"2022-09-16","cited_by_count":0,"area":null},{"id":"2307.12062","title":"Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations","created":"2023-07-22","cited_by_count":0,"area":null},{"id":"2308.08693","title":"AI planning in the imagination: High-level planning on learned abstract search spaces","created":"2023-08-16","cited_by_count":0,"area":null},{"id":"2309.03084","title":"Accelerating Nash Equilibrium Convergence in Monte Carlo Settings Through Counterfactual Value Based Fictitious Play","created":"2023-09-04","cited_by_count":0,"area":null},{"id":"2402.11835","title":"Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization","created":"2024-02-19","cited_by_count":0,"area":"deeprl"},{"id":"2410.11642","title":"Improve Value Estimation of Q Function and Reshape Reward with Monte Carlo Tree Search","created":"2024-10-15","cited_by_count":0,"area":"deeprl"},{"id":"2503.07013","title":"Learning Nash Equilibrial Hamiltonian for Two-Player Collision-Avoiding Interactions","created":"2025-03-10","cited_by_count":0,"area":null},{"id":"2504.18917","title":"Meta-Learning in Self-Play Regret Minimization","created":"2025-04-26","cited_by_count":0,"area":null},{"id":"2509.00923","title":"Robust Deep Monte Carlo Counterfactual Regret Minimization: Addressing Theoretical Risks in Neural Fictitious Self-Play","created":"2025-08-31","cited_by_count":0,"area":null},{"id":"2509.13653","title":"Efficient Last-Iterate Convergence in Regret Minimization via Adaptive Reward Transformation","created":"2025-09-17","cited_by_count":0,"area":null},{"id":"2509.23747","title":"Beyond Game Theory Optimal: Profit-Maximizing Poker Agents for No-Limit Holdem","created":"2025-09-28","cited_by_count":0,"area":null},{"id":"2510.25080","title":"Monopoly Deal: A Benchmark Environment for Bounded One-Sided Response Games","created":"2025-10-29","cited_by_count":0,"area":null},{"id":"2511.12083","title":"No-Regret Strategy Solving in Imperfect-Information Games via Pre-Trained Embedding","created":"2025-11-15","cited_by_count":0,"area":null},{"id":"2511.12089","title":"KrwEmd: Revising the Imperfect-Recall Abstraction from Forgetting Everything","created":"2025-11-15","cited_by_count":0,"area":null},{"id":"2512.15435","title":"Outer-Learning Framework for Playing Multi-Player Trick-Taking Card Games: A Case Study in Skat","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2602.15817","title":"Solving Parameter-Robust Avoid Problems with Unknown Feasibility using Reinforcement Learning","created":"2026-02-17","cited_by_count":0,"area":"deeprl"},{"id":"2603.03252","title":"Valet: A Standardized Testbed of Traditional Imperfect-Information Card Games","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2605.16312","title":"When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning","created":"2026-05-04","cited_by_count":0,"area":"deeprl"},{"id":"2605.19235","title":"GAE Falls Short in Imperfect-Information Self-Play Reinforcement Learning","created":"2026-05-19","cited_by_count":0,"area":"deeprl"},{"id":"2605.28863","title":"Self-Play Reinforcement Learning under Imperfect Information in Big 2","created":"2026-05-21","cited_by_count":0,"area":"deeprl"},{"id":"2605.30854","title":"Safe Equilibrium Policy Optimization for Strategic Agent Policies","created":"2026-05-29","cited_by_count":0,"area":"agents"},{"id":"2606.11284","title":"Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria","created":"2026-06-09","cited_by_count":0,"area":"deeprl"},{"id":"2607.06854","title":"A Gold-Standard Study of What Makes a Lightweight Game-Playing Agent Strong","created":"2026-07-07","cited_by_count":0,"area":"agents"},{"id":"2607.10814","title":"Auditing Belief-Conditioned LLM Agents in Hidden-Information Social Deduction Games","created":"2026-07-12","cited_by_count":0,"area":"agents"},{"id":"2608.08407","title":"Does a Toehold Make a Bidder Bolder? Preemption and Multiplicity in Multi-Round Takeover Auctions","created":"2026-08-09","cited_by_count":0,"area":null},{"id":"2609.03660","title":"Local Updates, Global Learning (LUGL): Playing Games with non-incremental Learners","created":"2026-09-03","cited_by_count":0,"area":null},{"id":"2609.11923","title":"GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay","created":"2026-09-10","cited_by_count":0,"area":null}],"next_cursor":"100"}