{"total":111,"rows":[{"id":"2107.12808","title":"Open-Ended Learning Leads to Generally Capable Agents","created":"2021-07-27","cited_by_count":182,"area":null},{"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":"2106.04615","title":"Vector Quantized Models for Planning","created":"2021-06-08","cited_by_count":48,"area":null},{"id":"2106.15691","title":"Deep Multiagent Reinforcement Learning: Challenges and Directions","created":"2021-06-29","cited_by_count":42,"area":null},{"id":"2203.11973","title":"Scalable Deep Reinforcement Learning Algorithms for Mean Field Games","created":"2022-03-22","cited_by_count":38,"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":"2103.04564","title":"Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization","created":"2021-03-08","cited_by_count":35,"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":"2110.13827","title":"Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization","created":"2021-10-26","cited_by_count":33,"area":null},{"id":"2104.02921","title":"Unsupervised Visual Attention and Invariance for Reinforcement Learning","created":"2021-04-07","cited_by_count":30,"area":null},{"id":"2106.14326","title":"Last-iterate Convergence in Extensive-Form Games","created":"2021-06-27","cited_by_count":28,"area":null},{"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":"2106.07551","title":"MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning","created":"2021-06-05","cited_by_count":23,"area":null},{"id":"2102.09812","title":"Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space","created":"2021-02-19","cited_by_count":20,"area":null},{"id":"2106.00123","title":"Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review","created":"2021-05-31","cited_by_count":17,"area":null},{"id":"2203.16406","title":"PerfectDou: Dominating DouDizhu with Perfect Information Distillation","created":"2022-03-30","cited_by_count":15,"area":null},{"id":"2301.06085","title":"Learning Near-Optimal Intrusion Responses Against Dynamic Attackers","created":"2023-01-11","cited_by_count":15,"area":null},{"id":"2306.16884","title":"Policy Space Diversity for Non-Transitive Games","created":"2023-06-29","cited_by_count":15,"area":null},{"id":"2306.00087","title":"Adaptive Coordination in Social Embodied Rearrangement","created":"2023-05-31","cited_by_count":14,"area":null},{"id":"2402.12499","title":"Automated Security Response through Online Learning with Adaptive Conjectures","created":"2024-02-19","cited_by_count":13,"area":null},{"id":"2407.00401","title":"PUZZLES: A Benchmark for Neural Algorithmic Reasoning","created":"2024-06-29","cited_by_count":13,"area":null},{"id":"2103.04539","title":"Model-Free Online Learning in Unknown Sequential Decision Making Problems and Games","created":"2021-03-08","cited_by_count":12,"area":null},{"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":"2303.04096","title":"Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play","created":"2023-03-07","cited_by_count":10,"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":"2311.01642","title":"Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula","created":"2023-11-03","cited_by_count":10,"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":"2304.03443","title":"Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Drones","created":"2023-04-07","cited_by_count":9,"area":null},{"id":"2309.03292","title":"Scalable Learning of Intrusion Responses through Recursive Decomposition","created":"2023-09-06","cited_by_count":8,"area":null},{"id":"2406.01103","title":"Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment","created":"2024-06-03","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":"2204.02558","title":"DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning","created":"2022-04-06","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":"2310.07745","title":"Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey","created":"2023-10-11","cited_by_count":7,"area":null},{"id":"2601.03335","title":"Digital Red Queen: Adversarial Program Evolution in Core War with LLMs","created":"2026-01-06","cited_by_count":7,"area":null},{"id":"2302.00797","title":"Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning","created":"2023-02-01","cited_by_count":6,"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":"2203.10045","title":"Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty","created":"2022-03-18","cited_by_count":5,"area":null},{"id":"2205.14694","title":"Learning Security Strategies through Game Play and Optimal Stopping","created":"2022-05-29","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":"2304.01547","title":"Regularization of the policy updates for stabilizing Mean Field Games","created":"2023-04-04","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":"2110.01810","title":"Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess","created":"2021-10-05","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":"2102.05026","title":"Multi-Agent Coordination in Adversarial Environments through Signal Mediated Strategies","created":"2021-02-09","cited_by_count":3,"area":null},{"id":"2104.03154","title":"Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network","created":"2021-04-07","cited_by_count":3,"area":null},{"id":"2201.07224","title":"NSGZero: Efficiently Learning Non-Exploitable Policy in Large-Scale Network Security Games with Neural Monte Carlo Tree Search","created":"2022-01-17","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":"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":"2208.02029","title":"Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess","created":"2022-08-03","cited_by_count":3,"area":null},{"id":"2210.02205","title":"Game Theoretic Rating in N-player general-sum games with Equilibria","created":"2022-10-05","cited_by_count":3,"area":null},{"id":"2304.13138","title":"The Update-Equivalence Framework for Decision-Time Planning","created":"2023-04-25","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":"2106.15868","title":"Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents","created":"2021-06-30","cited_by_count":2,"area":null},{"id":"2208.08552","title":"A Framework for Understanding and Visualizing Strategies of RL Agents","created":"2022-08-17","cited_by_count":2,"area":null},{"id":"2209.10958","title":"Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments","created":"2022-09-22","cited_by_count":2,"area":null},{"id":"2210.09646","title":"RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning","created":"2022-10-18","cited_by_count":2,"area":null},{"id":"2212.07967","title":"Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet","created":"2022-12-15","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":"2308.15550","title":"Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning","created":"2023-08-29","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":"2604.25796","title":"StratFormer: Adaptive Opponent Modeling and Exploitation in Imperfect-Information Games","created":"2026-04-28","cited_by_count":2,"area":null},{"id":"2105.12328","title":"D2CFR: Minimize Counterfactual Regret with Deep Dueling Neural Network","created":"2021-05-26","cited_by_count":1,"area":null},{"id":"2105.13806","title":"DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future","created":"2021-04-20","cited_by_count":1,"area":null},{"id":"2208.09452","title":"Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games","created":"2022-08-19","cited_by_count":1,"area":null},{"id":"2302.10058","title":"Differentiable Arbitrating in Zero-sum Markov Games","created":"2023-02-20","cited_by_count":1,"area":null},{"id":"2311.13414","title":"From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?","created":"2023-11-22","cited_by_count":1,"area":null},{"id":"2311.15089","title":"Where2Start: Leveraging initial States for Robust and Sample-Efficient Reinforcement Learning","created":"2023-11-25","cited_by_count":1,"area":null},{"id":"2311.16339","title":"Reward Shaping for Improved Learning in Real-time Strategy Game Play","created":"2023-11-27","cited_by_count":1,"area":null},{"id":"2404.11144","title":"Self-adaptive PSRO: Towards an Automatic Population-based Game Solver","created":"2024-04-17","cited_by_count":1,"area":null},{"id":"2404.13150","title":"Transformer Based Planning in the Observation Space with Applications to Trick Taking Card Games","created":"2024-04-19","cited_by_count":1,"area":null},{"id":"2407.05864","title":"Neural Network-based Information Set Weighting for Playing Reconnaissance Blind Chess","created":"2024-07-08","cited_by_count":1,"area":null},{"id":"2502.04402","title":"Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks","created":"2025-02-06","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":"2604.03472","title":"Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution","created":"2026-04-03","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":"2605.22748","title":"Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning","created":"2026-05-21","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":"2607.27177","title":"Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork","created":"2026-07-29","cited_by_count":1,"area":null},{"id":"2104.10845","title":"Optimize Neural Fictitious Self-Play in Regret Minimization Thinking","created":"2021-04-22","cited_by_count":0,"area":null},{"id":"2201.11783","title":"Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks","created":"2022-01-27","cited_by_count":0,"area":null},{"id":"2206.07520","title":"Principal Trade-off Analysis","created":"2022-06-09","cited_by_count":0,"area":null},{"id":"2210.12896","title":"Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement Learning","created":"2022-10-24","cited_by_count":0,"area":null},{"id":"2212.13379","title":"Learning Individual Policies in Large Multi-agent Systems through Local Variance Minimization","created":"2022-12-27","cited_by_count":0,"area":null},{"id":"2302.14208","title":"Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments","created":"2023-02-28","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":"2404.14418","title":"Mitigating Cascading Effects in Large Adversarial Graph Environments","created":"2024-04-12","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":"2406.10411","title":"Tree Search for Simultaneous Move Games via Equilibrium Approximation","created":"2024-06-14","cited_by_count":0,"area":null},{"id":"2407.06485","title":"CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community","created":"2024-07-09","cited_by_count":0,"area":null},{"id":"2410.13769","title":"Transformer Guided Coevolution: Improved Team Selection in Multiagent Adversarial Team Games","created":"2024-10-17","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":"2510.22568","title":"SPIRAL: Self-Play Incremental Racing Algorithm for Learning in Multi-Drone Competitions","created":"2025-10-26","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}],"next_cursor":"100"}