Game-Playing Agents

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Game-playing agents study how AI can perceive game states, plan actions, and adapt to opponents, providing testbeds for decision-making under competition, uncertainty, and long time horizons. Common approaches include tree search and Monte Carlo Tree Search, self-play reinforcement learning, regret minimization for imperfect-information games, and language- or vision-language models equipped with memory, tools, and learned world models. Evaluation spans bounded games such as chess and Go, poker and social deduction, real-time strategy, and interactive video games, using measures such as win rate, exploitability, task progress, and performance against people. Specialized systems can be highly capable in defined settings, while broad generalization remains difficult: agents still struggle with visual grounding, reliable memory, opponent modeling, and timely action in unfamiliar or long-running games.

Latest papers

206 papers36 papers in the last four weeks, up 50% on the four weeks before. 0.2% of all new papers.

Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning
Jun 3, 2026Zicheng Zhao, Yu Lan, Chengzhengxu Li +2Reinforcement Learning Game-Playing Agents
Xi’an Jiaotong University · Queen Mary University of London
ChessMimic: Per-Rating Transformer Models for Human Move, Clock, and Outcome Prediction in Online Blitz Chess
Jun 3, 2026Thomas JohnsonGame-Playing Agents Transformer
Nascent
Safe Equilibrium Policy Optimization for Strategic Agent Policies
May 29, 2026Karthika Arumugam, Kiran Kumar Manku, Amit DhandaMulti-Agent Reinforcement Learning Game-Playing Agents
†Amazon, USA
PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers
May 28, 2026Boning Li, Baoxiang Wang, Longbo HuangGame-Playing Agents Imperfect-Information Games
IIIS, Tsinghua University · The Chinese University of Hong Kong, Shenzhen
PTCG-Bench: Can LLM Agents Master Pokémon Trading Card Game?
May 28, 2026Dongdong Hua, Yifei Sun, Renhong Huang +3Game-Playing Agents LLM Agent Evaluation
Zhejiang University · FinVolution Group
UniMaia: Steering Chess Policies with Language for Human-like Play
May 26, 2026Sherman Siu, Lesley IsteadGame-Playing Agents Language Model-Based Control
University of Waterloo · Carleton University
Explore Before You Solve: The Speed--Depth Trade-off in Epistemic Agents for ARC-AGI-3
May 25, 2026Liew Keong HanGame-Playing Agents AI Agent Evaluation
Independent researcher.
MAPLE: Multi-State Aggregated Policy Evaluation for AlphaZero in Imperfect-Information Games
May 22, 2026Qian-Rong Li, Hung Guei, I-Chen Wu +1Game-Playing Agents Imperfect-Information Games
Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan · Institute of Information Science, Academia Sinica, Taiwan
One Policy, Infinite NPCs: Persona-Traceable Shared RL Policies for Scalable Game Agents
May 22, 2026Yoosung HongReinforcement Learning Game-Playing Agents
Independent Researcher
Symbolic Reasoning Frameworks Trigger Memory-Mediated Ecosystem Dynamics in Multi-Agent LLM Systems
May 22, 2026Augustin ChanMulti-Agent LLM Systems Game-Playing Agents
Self-Play Reinforcement Learning under Imperfect Information in Big 2
May 21, 2026Aalok PatwaGame-Playing Agents Self-Play RL
University of Pennsylvania
Not Yet: Humans Outperform LLMs in a Colonel Blotto Tournament
May 21, 2026Dmitry Dagaev, Egor Ivanov, Petr Parshakov +2Game-Playing Agents Game Theory
HSE University · New Economic School · Central Economic Mathematical Institute, Russian Academy of Sciences +3
For How Long Should We Be Punching? Learning Action Duration in Fighting Games
May 20, 2026Hoang Hai Nguyen, Kurt Driessens, Dennis J. N. J. SoemersReinforcement Learning Game-Playing Agents
Department of Advanced Computing Sciences, Maastricht University, Maastricht, the Netherlands
Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX
May 20, 2026Soichiro Nishimori, Shinri Okano, Keigo Habara +3RL Benchmarks Game-Playing Agents
The University of Tokyo, Tokyo, Japan. · Nara Institute of Science and Technology, Nara, Japan. · Independent Researcher. +5
Real-Time Parallel Counterfactual Regret Minimization
May 19, 2026Boning Li, Longbo HuangGame-Playing Agents GPU Acceleration
IIIS, Tsinghua University
Chessformer: A Unified Architecture for Chess Modeling
May 18, 2026Daniel Monroe, George Eilender, Philip Chalmers +2Transformer Interpretability Game-Playing Agents
University of Toronto · Williams College
SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents
May 18, 2026Wencan Jiang, Jiangning Zhang, Jianbiao Mei +6Game-Playing Agents Feedback Control
1Zhejiang University · 2National University of Singapore · 3Nanyang Technological University
From Imitation to Interaction: Mastering Game of Schnapsen with Shallow Reinforcement Learning
May 16, 2026Ján Klačan, Sizhong ZhangReinforcement Learning Game-Playing Agents
Vrije Universiteit Amsterdam
Cattle Trade: A Multi-Agent Benchmark for LLM Bluffing, Bidding, and Bargaining
May 14, 2026Robert Müller, Clemens MüllerMulti-Agent Negotiation Game-Playing Agents
Watermarking Game-Playing Agents in Perfect-Information Extensive-Form Games
May 14, 2026Juho Kim, Fei Fang, Tuomas SandholmGame-Playing Agents
Computer Science Department Carnegie Mellon University · Software and Societal Systems Department Carnegie Mellon University · Computer Science Department, CMU +3
ASH: Agents that Self-Hone in Long-Horizon Worlds
May 14, 2026Benjamin Schneider, Xavier Schneider, Victor Zhong +1Game-Playing Agents Self-Improving Agents
University of Waterloo · National Research Council Canada
Toward Modeling Player-Specific Chess Behaviors
May 12, 2026Loris Sogliuzzo, Aloïs Rautureau, Eric PietteGame-Playing Agents AI Agent Evaluation
ICTEAM, UCLouvain · Louvain-la-Neuve, Belgium
Continual Harness: Online Adaptation for Self-Improving Foundation Agents
May 11, 2026Seth Karten, Joel Zhang, Tersoo Upaa +5Continual Learning for LLM Agents Game-Playing Agents
Princeton University · ARISE Foundation · Google DeepMind
Towards Generalist Game Agents: A Survey of Foundation Models in Virtual Worlds
May 11, 2026Kuan Zhang, Dongchen Liu, Qiyue Zhao +12Game-Playing Agents AI Agent Benchmarks
College of AI, Tsinghua University · MMLab, The University of Hong Kong · University of Chinese Academy of Sciences
AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Play
May 9, 2026Vlad Murgoci, Matthijs Spaan, Yaniv OrenOpponent Modeling Game-Playing Agents
Delft University of Technology · CD Delft, The Netherlands · Department of Intelligent Systems
Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
May 8, 2026Botos Csaba, Sreejan Kumar, Austin Tudor David Andrews +6Game-Playing Agents Human Behavior Simulation
University of Oxford · Columbia University · New York University +2
Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark
May 7, 2026Cristiano da Costa Cunha, Ajmal Mian, Tim French +1RL Benchmarks Structural Causal Models
Department of Computer Science and Software Engineering, University of Western Australia, 6009 WA Australia
Strat-Reasoner: Reinforcing Strategic Reasoning of LLMs in Multi-Agent Games
May 6, 2026Yidong He, Yutao Lai, Pengxu Yang +4Game-Playing Agents RL for Language Model Reasoning
School of Software Engineering, South China University of Technology · Department of Computer Science, University of Oxford
Agent Island: A Saturation- and Contamination-Resistant Benchmark from Multiagent Games
May 5, 2026Connacher MurphyGame-Playing Agents Benchmark Design
Stanford University
A Structural Threshold in Decision Capacity Governs Collapse in Self-Play Reinforcement Learning
May 4, 2026Arahan KujurGame-Playing Agents Self-Play RL
Independent Researcher

Jun 3, 2026J/K move · Enter open · S save

Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning

Zicheng Zhao, Yu Lan, Chengzhengxu Li, Zhaohan Zhang+1

Xi’an Jiaotong University · Queen Mary University of London

Reinforcement Learning Game-Playing Agents Sparse-Reward RL Cooperative MARL

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Cooperative Multi-Agent Reinforcement Learning (MARL) frequently suffers from severe reward sparsity and exploration bottlenecks. While episodic memory mechanisms mitigate these issues by reusing high-return trajectories, they often trap agents in local optima due to unconstrained incentive distribution and semantic representation collapse. To address this, we propose Episodic Memory Temporal Consistency (EMTC), a framework that robustly constructs and selectively leverages historical experiences. EMTC introduces two synergistic components: (1) a Temporally Consistent Semantic Embedder that integrates contrastive learning with time-conditioned state reconstruction, preventing representation collapse and enabling precise memory retrieval; and (2) a Temporal Consistency Gating Mechanism that dynamically modulates episodic incentives based on temporal consistency error. This adaptive gate filters misleading signals from pseudo-successful trajectories, effectively mitigating Q-value overestimation. We provide theoretical guarantees, establishing a strict error bound that directly links the observable temporal consistency error to the underlying trajectory optimality and representation quality. Extensive evaluations on the SMAC and GRF benchmarks demonstrate that EMTC consistently outperforms state-of-the-art baselines. Notably, compared to the strongest episodic baseline, EMTC achieves absolute win-rate improvements of up to 24% in super-hard SMAC scenarios and an average improvement of 28% across GRF tasks.