The paper introduces Bidirectional Voronoi-biased Exploration Curriculum (BVER), a method that expands start states from the goal and goals from the initial state simultaneously, guiding both toward each other to train a single goal-conditioned policy. Inspired by bidirectional RRT planning, BVER biases exploration toward unexplored task space and steers the two expansions together. Experiments on point-mass mazes, quadrupedal box climbing, and robot-arm ring-on-peg transfer show that BVER learns faster than other reference-free curricula, achieving high success rates and robustness without requiring demonstrations.
By Juri Pfammatter, Kaixian Qu, Clemens Schwarke, Victor Klemm, Marco Hutter
arXiv:2607. 18296v1 Announce Type: new Abstract: Baghchal is a two-player asymmetric board game with Nepali origins where four tigers are to capture goats and twenty goats desire to keep tigers in immobility.
By Ranjit Raut, Aarav Subedi, Sagun Rai, Aaryan Shakya, Manoj Shakya
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:2611.07130v1 Announce Type: new
Abstract: Reinforcement learning has achieved strong performance in many tasks by letting agents learn from nothing more than a reward signal. However, in more c...
By Shan Yang
PhantomEnvironments is a framework that trains large language model agents in synthetic, rule‑generated fictional worlds. By creating multi‑turn reinforcement learning environments where agents search templated articles to answer multi‑hop questions, the approach eliminates the need for costly human data or hallucinated LLM‑generated settings. Agents trained in these zero‑cost, purely rule‑based worlds transfer effectively to real‑world multi‑hop search benchmarks, often surpassing models trained on real data, and demonstrate scalable search behavior that grows linearly with question difficulty.
By Anmol Kabra, Swathi Saravana Selvam, Albert Gong, Chao Wan, Christian Belardi, Dongyoung Go, Katie Z. Luo, Kilian Q. Weinberger
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:2610.20584v1 Announce Type: new
Abstract: Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their...
By Mingyu Ouyang, Yiran Wang, Siyuan Hu, Xingyilang Yin, Huiqiang Sun, Guangzhi Wang, Junfu Pu, Cheng Cheng, Honghao Chen, Ying Shan, Mike Zheng Shou, Hwee Tou Ng
arXiv:2305.17327v4 Announce Type: replace
Abstract: Imperfect Information Games (IIGs) are used to model games under uncertainty or lack complete information. Counterfactual Regret Minimization (CFR)...
By Jiayu Chen, Xudong Wu, Zhekai Wang, Vaneet Aggarwal
arXiv:2605.14211v4 Announce Type: replace
Abstract: Long-horizon visuomotor tasks remain a fundamental challenge in AI, as current methods rely on hand-engineered rewards or action-labeled demonstrat...
By Benjamin Schneider, Xavier Schneider, Victor Zhong, Sun Sun
The paper introduces Adversarial Heuristic Learning (AHL), a framework that uses AI agents to refine game policies without changing model weights. It presents AAArena, a benchmark of 12 adversarial games and 1,920 human programs, where agents analyze replays, select opponents, and revise policies to maximize ranking within limited budgets. Experiments show that certain configurations achieve gold medals, while performance drops in games with complex rules, highlighting challenges in game understanding and long‑horizon policy development.
By Kaisen Yang, Qingle Liu, Kejin Wang, Yicheng Zhao, Jieming Li, Shenghan Zheng, Ruize Yang, Bojun Yang, Heng Gong, Xiang Gao, Lanyue Zhang, Kaiyu Zhong, Zhuo Liu, Shaoxuan Li, Chengxi Li, Yong Yan, Weixuan Zhang, Tianwei Luo, Situ Wang, Youjie Zheng, Sihan Zhao, Shengyuan Wang, Huan-ang Gao, Jiazheng Xu, Xiaohui Xie, Wentao Han, Hongning Wang
The paper introduces Rollback, a method that uses action‑conditioned world models to backtrack to critical decision points, branch into alternative actions, and compare outcomes to attribute success to individual decisions. By turning world models into counterfactual engines, Rollback provides fine‑grained, action‑level supervision without a separate value model and integrates distributed world‑model search with asynchronous real‑robot execution. Experiments on block extraction, ball tossing, and tabletop curling show that Rollback improves success rates by over 10% compared to the strongest baseline and surpasses human performance on two tasks within eight hours of training.
By Jiayi Luo, Yang Zhang, Liliang Chen, Nan Wang, Chen Gao, Boxiang Qiu, Zhuoxuan Li, Baorui Peng, Chuankang Li, Yuxiang Yan, Si Liu, Qingyun Sun, Shuicheng Yan, Maoqing Yao, Jianxin Li, Guanghui Ren
arXiv:2609.15851v1 Announce Type: new
Abstract: Language models can learn from experience, but raw solution trajectories are often too long and noisy to provide effective guidance. In this work, we p...
By Guanheng Chen, Tianzhu Ye, Li Dong, Xun Wu, Shaohan Huang, Furu Wei