arXiv cs.AI论文
通过转移复杂度刻画游戏世界
本文提出转移复杂度剖面（TCP），用于量化游戏环境或游戏数据集中转移预测问题的难度。TCP 包含一组可复现的指标，涵盖单步分支、交互不确定性、时空依赖跨度等，并附带参考分布和测量预算，使不同基准间的比较成为可能。作者呼吁将 TCP 作为游戏世界建模和强化学习论文的标准元数据。
这篇是正式发表的长论文，站内提供中文解读，全文请到原文阅读 PDF。
Abstract:Game world modeling (GWM) and reinforcement learning (RL) are often confounded because research papers rarely quantify how difficult the underlying transition prediction problem is at the declared interface (pixels/tokens/latents with finite history). We propose the Transition Complexity Profile (TCP): a small, reproducible set of metrics that characterizes an environment's (or gameplay dataset's) induced transition kernel by (i) intrinsic one-step branching, (ii) interaction-induced uncertainty and opponent influence when observable, and (iii) temporal/spatial dependency span via standardized probe curves. TCP is reported with an explicit reference distribution, protocol stochasticity, and a versioned measurement budget (sampling/resampling and fixed probe compute), enabling comparable numbers across benchmarks. We outline how common game families and modern "neural game engine" domains populate this landscape and call for TCP to become standard benchmark metadata and a required statistic in GWM and RL papers.