The paper introduces LUGL (Local Updates, Global Learning), a framework that separates data collection from model fitting, allowing non‑incremental learners such as gradient‑boosted trees (LightGBM) to be used in reinforcement learning for games. LUGL alternates between a local update phase—where agents play self‑play games and store tabular updates—and a global learning phase—where a function approximator is trained on the accumulated table before it is reset. Experiments on both perfect‑information and imperfect‑information games show that LightGBM‑based agents perform competitively or better than neural‑network baselines like DQN and DeepCFR.
By David Milec, Spyridon Samothrakis, Michael Fairbank, Dennis J. N. J. Soemers
arXiv:2610.13995v1 Announce Type: cross
Abstract: Post-training large language models (LLMs) on two-player zero-sum games provides a verifiable reward, and the gains have been shown to transfer to ge...
By Weixian Waylon Li, Jiaxin Zhang, Han Zhou, Chengzu Li, Ke Wang, Fengxiang He, Yiwen Guo
arXiv:2607. 06854v1 Announce Type: cross Abstract: Reinforcement learning agents for imperfect-information card games are only as strong as the opponents they train against, and they are hard to grade, since they beat a random opponent over 99 percent of the time and only tie copies of themselves.
By Nima Kelidari, Mohammadsaeed Haghi, Mahdi Salmani
arXiv:2610.18354v1 Announce Type: new
Abstract: Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capabil...
By Arastun Mammadli, Herbert Woisetschl\"ager, Jawad Fayaz, Shiqiang Wang
arXiv:2607. 14171v1 Announce Type: new Abstract: Reinforcement learning has emerged as the dominant paradigm for training large language model (LLM) agents that interact with executable sandboxes.
By Bowei He, Yankai Chen, Xiaokun Zhang, Xue Liu
arXiv:2610.00592v1 Announce Type: cross
Abstract: In partially observable reinforcement learning (RL), a later observation can make stored information obsolete or change what it implies for the next...
By Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov, Alexey K. Kovalev
The paper investigates the use of Jev, a decision model that produces calibrated, typed answers in a single forward pass, as a component in reinforcement learning (RL). It shows that Jev can satisfy most requirements of RL systems except providing a value function, and proposes algorithms that use Jev as a reference policy, exploration judge, and replay rater. Experiments on MiniGrid and Atari tasks demonstrate that incorporating Jev improves sample efficiency, exploration, and learning performance compared to a standard RL learner, even when the learner alone fails to progress.
By Yi Ma, Tianpei Yang, Yaodong Yang, Weixun Wang, Hongyao Tang
arXiv:2607. 23333v1 Announce Type: cross Abstract: We revisit the regret loss framework introduced in Park et al.
By Chanwoo Park, Asuman Ozdaglar
arXiv:2609.09094v1 Announce Type: new
Abstract: Combining search with function approximation has driven major advances in game-playing programs, making self-play algorithms more competitive than ever...
By Raphael Boige, Amine Boumaza, Bruno Scherrer
arXiv:2609.00892v1 Announce Type: new
Abstract: Rubric-based reinforcement learning decomposes open-ended instructions into prompt-specific, flexible rubrics, making it better suited than reinforceme...
By Siyuan Li, Xinxin Song, Chen Ruinian, Jingjing Fan, Tingxiong Xiao, Yangen Hu, Ke Zeng, Jinli Suo
Selective Growing Q‑Networks (SGQN) extend value‑based Q‑learning for continuous control by replacing uniform action‑grid growth with an adaptive grid that adds bins near high‑Q regions and prunes low‑Q bins, while maintaining decoupled Q‑learning and epsilon‑greedy action selection. On eight DeepMind Control Suite tasks, SGQN achieves the highest mean final return on four tasks, matches the best method within 1.1% on three more, and uses on average 7.5 of 9 available bins per dimension. Experiments show that the adaptive grid’s growth and pruning have limited impact on performance, whereas starting from a coarse grid and using epsilon‑greedy selection are critical for success.
By Parham Nooranbakht, Seyed Mahdi Basiri Azad, Joschka B\"odecker
arXiv:2506. 13862v2 Announce Type: replace-cross Abstract: In Reinforcement Learning (RL), regularization with a Kullback-Leibler divergence that penalizes large deviations between successive policies has emerged as a popular tool both in theory and practice.
By Alex Davey, Alena Shilova, Brahim Driss, Riad Akrour