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.

The PokeAgent Challenge: Competitive and Long-Context Learning at Scale
Mar 16, 2026Seth Karten, Jake Grigsby, Tersoo Upaa +28AI Agent Benchmarks Game-Playing Agents
Princeton · UT-Austin · Team Heatz +12
Evolutionary Transfer Learning for Dragonchess
Mar 16, 2026Jim O'Connor, Annika Hoag, Sarah Goyette +1Game-Playing Agents Transfer Learning
Computer Science Department, Connecticut College, New London, United States
See, Symbolize, Act: Grounding VLMs with Spatial Representations for Better Gameplay
Mar 12, 2026Ashish Baghel, Paras ChopraVision-Language Agents Vision-Language Grounding
Lossfunk
Resource-constrained Amazons chess decision framework integrating large language models and graph attention
Mar 11, 2026Tianhao Qian, Zhuoxuan Li, Jinde Cao +2Game-Playing Agents Weak-to-Strong Generalization
School of Mathematics, Southeast University, Nanjing, 210096, China · Systems Research Institute of the Polish Academy of Sciences, Warsaw 01-447, Poland · School of Cyber Science & Engineering, Southeast University, Nanjing, 210096, China +2
MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games
Mar 9, 2026Yunfei Xie, Kevin Wang, Bobby Cheng +9Multi-Agent LLM Systems LLM Agent Self-Improvement
Rice University · The University of Texas at Austin · A*STAR +3
Oracle-Guided Soft Shielding for Safe Move Prediction in Chess
Mar 9, 2026Prajit T Rajendran, Fabio Arnez, Huascar Espinoza +2Game-Playing Agents Safety-Critical Control
Universit´e Paris-Saclay, CEA LIST Palaiseau, France · Chips JU Brussels, Belgium · Laboratoire National de Metrologie et d’Essais Trappes, France
Enhancing Consistency of Werewolf AI through Dialogue Summarization and Persona Information
Mar 7, 2026Yoshiki Tanaka, Takumasa Kaneko, Hiroki Onozeki +6Game-Playing Agents Large Language Model-Based Role-Play Simulation
The University of Electro-Communications
Ailed: A Psyche-Driven Chess Engine with Dynamic Emotional Modulation
Mar 5, 2026Diego Armando Resendez PradoGame-Playing Agents Human Behavior Simulation
Independent Researcher
COMBAT: Conditional World Models for Behavioral Agent Training
Feb 28, 2026Anmol Agarwal, Pranay Meshram, Sumer Singh +5Action-Conditioned World Models World Model Learning
Overworld AI · Indian Institute of Science Education and Research Bhopal
Generalized Rapid Action Value Estimation in Memory-Constrained Environments
Feb 26, 2026Aloïs Rautureau, Tristan Cazenave, Éric PietteMonte Carlo Tree Search Game-Playing Agents
ICTEAM, UCLouvain, Louvain-la-Neuve, Belgium · LAMSADE, Université Paris-Dauphine, Paris, France
Regret-Guided Search Control for Efficient Learning in AlphaZero
Feb 24, 2026Yun-Jui Tsai, Wei-Yu Chen, Yan-Ru Ju +2Reinforcement Learning Game-Playing Agents
Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan · Institute of Information Science, Academia Sinica, Taiwan · College of Computing, Georgia Institute of Technology
(Perlin) Noise as AI coordinator
Feb 21, 2026Kaijie Xu, Clark VerbruggeMulti-Agent Control Multi-Agent Coordination
McGill University, Montreal, Quebec, Canada
AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games
Feb 19, 2026Lance Ying, Ryan Truong, Prafull Sharma +9Artificial General Intelligence AI Agent Benchmarks
MIT · Harvard University · University of British Columbia +3
Two-Bridge: Exclusive Objectives and Extended Horizon StarCraft II Benchmark
Feb 19, 2026Sourav Panda, Tanmay Ambadkar, Shreyash Kale +2RL Benchmarks Game-Playing Agents
Pennsylvania State University, PA, USA.
World Models for Policy Refinement in StarCraft II
Feb 16, 2026Yixin Zhang, Ziyi Wang, Yiming Rong +6Game-Playing Agents Action-Conditioned World Models
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences
Scalable Decision Making for Games of Imperfect Information
Nov 10, 2025Samuel Sokota, Eugene Vinitsky, Hengyuan Hu +3Game-Playing Agents Inference-Time Search
Carnegie Mellon University · NYU Tandon School of Engineering · Stanford University +1
Toward Agents That Reason About Their Computation
Oct 26, 2025Adrian Orenstein, Jessica Jiajing Chen, Gwyneth Anne Delos Santos +2Reinforcement Learning Game-Playing Agents
Department of Computing Science, University of Alberta, Canada · Alberta Machine Intelligence Institute (Amii) · Department of Computer Science, University of Toronto, Canada +1
The Algorithm Is Not the Behavior: Learned Priors Override Look-Ahead in a Chess-Playing Neural Network
Aug 29, 2025Elias Sandmann, Sebastian Lapuschkin, Wojciech SamekNeural Algorithmic Reasoning Neural Network Interpretability
Fraunhofer HHI · TU Dublin · TU Berlin
PuzzleJAX: A Benchmark for Reasoning and Learning
Aug 22, 2025Sam Earle, Graham Todd, Yuchen Li +5RL Benchmarks Game-Playing Agents
New York University · University of Malta · University of the Witwatersrand +1
Unbeatable imitation of a friend
Jul 22, 2025Masahiko UedaGame-Playing Agents Game Theory
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 1677-1, Yoshida, Yamaguchi, 753-8511, Japan.
StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley
Jul 10, 2025Weihao Tan, Changjiu Jiang, Yu Duan +5Game-Playing Agents Agent Evaluation
Nanyang Technological University, Singapore · Harbin Institute of Technology, Shenzhen, China · The Chinese University of Hong Kong, Shenzhen, China +1
Policy Improvement with Style-Specific Demonstrations
Jun 20, 2025Lingfeng Li, Yunlong Lu, Yongyi Wang +1Reinforcement Learning Game-Playing Agents
School of Computer Science, Peking University, Beijing 100871, China
Reinforcement learning for Quantum Tiq-Taq-Toe
Nov 10, 2024Catalin-Viorel Dinu, Thomas MoerlandReinforcement Learning Game-Playing Agents
Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Nov 16, 2023Alexander Rutherford, Benjamin Ellis, Matteo Gallici +18RL Benchmarks Reinforcement Learning
University of Oxford · Universitat Politècnica de Catalunya · University College London +2
Expected flow networks in stochastic environments and two-player zero-sum games
Oct 4, 2023Marco Jiralerspong, Bilun Sun, Danilo Vucetic +4Game-Playing Agents Zero-Sum Games
Mila – Qu´ebec AI Institute, Universit´e de Montr´eal
Playing 20 Question Game with Policy-Based Reinforcement Learning
Aug 23, 2018Huang Hu, Xianchao Wu, Bingfeng Luo +4Reinforcement Learning Game-Playing Agents
1Peking University, Beijing, China · 3Microsoft Development Co., Ltd, Tokyo, Japan · 2Microsoft Corporation, Beijing, China

Mar 16, 2026J/K move · Enter open · S save

The PokeAgent Challenge: Competitive and Long-Context Learning at Scale

Seth Karten, Jake Grigsby, Tersoo Upaa, Junik Bae+27

Princeton · UT-Austin · Team Heatz+12

AI Agent Benchmarks Game-Playing Agents Long-Horizon Agent Evaluation

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We present the PokeAgent Challenge, a large-scale benchmark for decision-making research built on Pokemon's multi-agent battle system and expansive role-playing game (RPG) environment. Partial observability, game-theoretic reasoning, and long-horizon planning remain open problems for frontier AI, yet few benchmarks stress all three simultaneously under realistic conditions. PokeAgent targets these limitations at scale through two complementary tracks: our Battling Track, which calls for strategic reasoning and generalization under partial observability in competitive Pokemon battles, and our Speedrunning Track, which requires long-horizon planning and sequential decision-making in the Pokemon RPG. Our Battling Track supplies a dataset of 20M+ battle trajectories alongside a suite of heuristic, RL, and LLM-based baselines capable of high-level competitive play. Our Speedrunning Track provides the first standardized evaluation framework for RPG speedrunning, including an open-source multi-agent orchestration system for modular, reproducible comparisons of harness-based LLM approaches. Our NeurIPS 2025 competition validates both the quality of our resources and the research community's interest in Pokemon, with over 100 teams competing across both tracks and winning solutions detailed in our paper. Participant submissions and our baselines reveal considerable gaps between generalist (LLM), specialist (RL), and elite human performance. Analysis against the BenchPress evaluation matrix shows that Pokemon battling is nearly orthogonal to standard LLM benchmarks, measuring capabilities not captured by existing suites and positioning Pokemon as an unsolved benchmark that can drive RL and LLM research forward. We transition to a living benchmark with a live leaderboard for Battling and self-contained evaluation for Speedrunning at https://pokeagentchallenge.com.