Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition

Adversarial games have driven advances from heuristic search to reinforcement learning, yet learning and adapting strategies from limited samples remain challenging.

By Yang, Liu, Wang +24Oct 8, 2026

Score█████░░░░░4.5

Caveats

Preprint; not yet peer reviewed.

VerdictCompetent work. Briefs at most.

Read the original PDF Code

Abstract

Adversarial games have driven advances from heuristic search to reinforcement learning, yet learning and adapting strategies from limited samples remain challenging. AI agents offer an alternative by turning game experience into revisions of executable policies. Building on heuristic learning (HL), we formalize Adversarial Heuristic Learning (AHL), a paradigm that uses AI agents as learning engines to refine game policies and supporting software while keeping model weights fixed. We introduce AAArena, a benchmark comprising 12 authentic adversarial games and 1,920 archived human programs, with an evaluation protocol modeled on real-world game competitions. Agents interpret rules, choose opponents, analyze replays, and revise game agents to achieve their highest ranking within fixed match and evaluation budgets. We evaluate \val{completedmodels} model and harness configurations: Opus5.5 with Claude Code earns 6 gold medals, while no evaluated configuration tops the remaining 6 human ladders. Performance is generally weaker in games with more complex rule specifications. Further experiments show that opponent selection and dense feedback support policy improvement, and that agents learn from both on-policy replays of their own matches and off-policy replays of other players' matches. These results highlight HL's potential in adversarial games and identify persistent challenges in game understanding, strategy implementation, and long-horizon policy development.

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 editor's rubric

Heuristic review

Dimension	Level	Weight	What that level means
Leverage	███░░ 3	24%	A method or resource many groups across the field will adopt within a year.
Magnitude	██░░░ 2	18%	Solid incremental gain on a meaningful problem.
Evidence	██░░░ 2	14%	Limited: single setting, weak baselines, or an observational association presented as causal.
Novelty	██░░░ 2	16%	A new combination of known ideas.
Trajectory	██░░░ 2	18%	Some room to improve with obvious engineering.
Stakes	██░░░ 2	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); stakes (general AI).

How the score was computed

rank-2026-10-07

Score█████░░░░░4.5

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Merit: 3.7 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 3.9 / 10; Shrunk toward the desk prior by editor confidence (34%).
Attention: 67%; Citations, upvotes, points, mentions.
Freshness: 30%; Half-life decay since publication.

Citations0 (reference 15, via semantic-scholar, Oct 13, 2026, 01:15 UTC)
Influential citations0 (reference 3, via semantic-scholar, Oct 13, 2026, 01:15 UTC)
Hugging Face upvotes32 (reference 25, via hf-daily, Oct 12, 2026, 13:49 UTC)
GitHub stars24 (reference 250, via hf-daily, Oct 12, 2026, 13:49 UTC)

The record

Reviewed by heuristic-v6 on Oct 11, 2026, 02:05 UTC. Paper type: method.
Categories: cs.AI, cs.CL
BRIEF, No.7 in the Artificial Intelligence edition of October 13, 2026.