RL for Game AI

RL: Reinforcement Learning

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Latest papers

29 papers8 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Build, Don't Play: Self-Improving LLM Research Agents Develop a Hearthstone Battlegrounds AI That Rivals Top Players
Oct 24, 2026Changlian Ma, Limin WangLLM Agent Self-Improvement Autonomous Research Agents
Nanjing University · Shanghai AI Laboratory
HiSTEVE 1.0: Harnessing Human-Interface VLA Agents for Open-World Games
Oct 22, 2026Zecong Tang, Zian Guan, Yuncheng Guo +3RL for Game AI Game-Playing Agents
The University of Hong Kong · Zhejiang University · Fudan University +2
Player-1: Learning to Play from Synthetic Experience
Oct 14, 2026Mingyu Ouyang, Yiran Wang, Siyuan Hu +9Game-Playing Agents RL for Game AI
National University of Singapore · ARC Lab, Tencent IEG · Nanyang Technological University
Learning to Play: Reinforcement Learning and Games
Oct 12, 2026Aske PlaatRL for Game AI Reinforcement Learning
Learning Explainable Representations of Complex Game-playing Strategies
Oct 6, 2026Abhijeet Krishnan, Colin M. Potts, Arnav Jhala +3Game-Playing Agents Program Synthesis
Venture IV 420, North Carolina State University, Raleigh, NC 27606 USA · Department of Aerospace Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, Maharashtra, India · Tata Research Development and Design Centre, Pune 411013, Maharashtra, India +1
Factoriax: A GPU-Accelerated Factorio-Style Simulator for Reinforcement Learning
Oct 4, 2026Mickey Beurskens, Tristan Tomilin, Thiago D. SimãoRL Benchmarks Reinforcement Learning
Eindhoven University of Technology
LocusRL: Diagnosing LLM Reward and Policy Interventions in Competitive Games
Oct 3, 2026Chengyu Luan, Bo Xin, Songyan Guo +4RL for Game AI Reinforcement Learning
Harbin Institute of Technology
Mastering Atari 2600 Games with Discovered Options
Oct 2, 2026Erik M. Lintunen, Marlos C. MachadoRL for Game AI Hierarchical RL
University of Alberta · Alberta Machine Intelligence Institute (Amii) · Canada CIFAR AI Chair
The Surprising Effectiveness of Approximate Value Iteration in Self-Play
Sep 8, 2026Raphael Boige, Amine Boumaza, Bruno ScherrerGame-Playing Agents Self-Play RL
Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France
PlayTrain: An Efficient Reinforcement Learning Framework for LLM-Generated Adaptable JavaScript Games
Sep 8, 2026Ryan Truong, Lance Ying, Samuel J. Gershman +1Reinforcement Learning Game-Playing Agents
Harvard University, Cambridge, MA, USA · MIT, Department of Brain and Cognitive Sciences, Cambridge, MA, USA · Kempner Institute for the Study of Natural and Artificial Intelligence, Cambridge, MA, USA +1
PureTD: Reinforcement Learning for Backgammon Money Games with No Evaluation-time Search
Aug 15, 2026Alexander L. StrehlRL for Game AI Game-Playing Agents
Independent Researcher
Training a Conditioned Video Game Agent on a VLM Annotated Dataset
Aug 6, 2026Katrin Schmid, Iuri FrosioRL for Game AI Reinforcement Learning
NVIDIA, Australia · NVIDIA, Italy
CAST: Game Solvers as Turn-Level Teachers for LLM Agents
Jul 28, 2026Yu Wang, Yi-Kai Zhang, Wentao Shi +8Credit Assignment in RL Reinforcement Learning
University of Science and Technology of China · 4Meituan, China · 2Nanjing University +1
AlphaZero in Sparsely Rewarded Games: Limits and Auxiliary Supervision
Jul 9, 2026Brent Kong, Tejas Ram, Tony Yue YuGame-Playing Agents Self-Play RL
Department of Mathematics · California Institute of Technology · Department of Computer Science
A Gold-Standard Study of What Makes a Lightweight Game-Playing Agent Strong
Jul 7, 2026Nima Kelidari, Mohammadsaeed Haghi, Mahdi SalmaniSelf-Play RL Reinforcement Learning
University of Southern California
FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games
Jul 7, 2026Chase McDonald, Nathan Tsang, Wesley N. KerrRL Benchmarks Game-Playing Agents
Como Research · Riot Games
Coachable agents for interactive gameplay
Jul 1, 2026Roberto Capobianco, Harm van Seijen, Nolan D. Bard +38Reinforcement Learning RL for Game AI
Sony AI, Zurich, Switzerland · Sony AI, North America, various locations · Sony AI, Tokyo, Japan
Reinforcement Learning in Super Mario Bros: Curriculum, Pedagogy, and Optimal Level Design in World 1-1
Jun 28, 2026Jesse Ponnock, Lucas HoRL for Game AI Reinforcement Learning
Johns Hopkins University
Superhuman AI for Generals.io Using Self-Play Reinforcement Learning
Jun 22, 2026Matej Straka, Viliam Lisý, Martin SchmidGame-Playing Agents Self-Play RL
Department of Applied Mathematics, Charles University, Prague, Czech Republic · Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic · EquiLibre Technologies, Inc.
IRumAI: Reinforcement Learning for Indian Rummy
Jun 20, 2026Vignesh MohanReinforcement Learning Game-Playing Agents
EURECOM Sophia Antipolis, France
Augmenting Game AI with Deep Reinforcement Learning
Jun 18, 2026Alessandro Sestini, Joakim Bergdahl, Amir Baghi +3RL for Game AI Reinforcement Learning
Electronic Arts (EA), Stockholm, Sweden
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
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
MindGames Arena Generalization Track: In2AI Solution with Delayed Per-Step Reward Attribution
Apr 13, 2026Aliaksei Korshuk, Alexander Buyantuev, Ilya MakarovRL for Game AI RL for Language Models
iMak AI Lab · Coframe
PokeRL: Reinforcement Learning for Pokemon Red
Apr 12, 2026Dheeraj Mudireddy, Sai PatibandlaRL for Game AI Reinforcement Learning
Texas A&M Institute of Data Science Texas A&M University College Station, USA · Dept. of Computer Science Texas A&M University College Station, USA
Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning
Mar 4, 2026Thomas PravetzRL for Game AI Zero-Shot Policy Transfer
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
Octax: Accelerated CHIP-8 Arcade Environments for Reinforcement Learning in JAX
Oct 2, 2025Waris Radji, Thomas Michel, Hector PiteauRL Benchmarks Reinforcement Learning
Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRIStAL, France · Independent Researcher
A Generalist Hanabi Agent
Mar 17, 2025Arjun V Sudhakar, Hadi Nekoei, Mathieu Reymond +3RL for Game AI Cooperative MARL
Chandar Research Lab · Mila – Quebec AI Institute · Polytechnique Montréal +4

Oct 24, 2026J/K move · Enter open · S save

Build, Don't Play: Self-Improving LLM Research Agents Develop a Hearthstone Battlegrounds AI That Rivals Top Players

Changlian Ma, Limin Wang

Nanjing University · Shanghai AI Laboratory

LLM Agent Self-Improvement Autonomous Research Agents Game-Playing Agents RL for Game AI AI Agent Benchmarks

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Can LLM agents carry a hard real-world problem from raw data to a system that holds its own against people? We study this in Hearthstone Battlegrounds, an eight-player auto-battler with stochastic shops and battles, hidden information and nearly a thousand interacting cards. We present AutoTavern, a self-improving research system: specialised LLM research agents share a typed research memory, every claim passes verification gates, and live games close the loop, so the agents improve the simulator, opponents and policy and, from the same feedback, their own research process; a human sets goals and rules on ambiguous mechanics. In 20 days AutoTavern built TavernGym, the first faithful, full-content reproduction of the live game, and delivered 23 versions of TavernAgent, trained with PPO against opponents calibrated to real games. TavernAgent plays complete games on the live ladder by itself: its rating rose from 6,000 to about 15,000 and, on the public leaderboard's measure, its last 20 records average a placement of 1.60 with 80% first places, better than the server's ten highest-rated players on every statistic (2.34, 59%). TavernBench, 960 tasks in 40 scenarios, shows that the system the agents built is the strongest player: TavernAgent answers 91% of the play tasks against at most 63% for frontier LLMs, and decides in 0.03 s where they need up to 26 s; within the second a live turn allows, it keeps 90% and no LLM more than 26%. LLM agents build far better than they play. We will open-source TavernBench and TavernGym at https://github.com/MCG-NJU/AutoTavern.