arXiv：cs.LG· Oliver Obst, Frieder Stolzenburg·· 2 天前AI 评分35
在 CEM 中，世界模型同时也是候选方案生成机制
In CEM, a World Model Is Also a Proposal Mechanism
AI 导读
研究将交叉熵方法（CEM）中世界模型的两项作用分开评估：用模型分数选择动作序列，以及拟合下一轮采样的分布。
正文
Abstract:The cross-entropy method (CEM) uses world-model scores to select action sequences and fit the distribution sampled in its next iteration. A scoring error can therefore change both the present decision and the candidates considered later. We evaluate these two roles separately. Four types of predictive model generate CEM traces, and every model rescores every saved candidate pool. Executing the same candidates in the environment provides a reference elite set and proposal update.
Across twelve independently trained task-seed units on Walker and Cheetah, the pre-specified proposal distance falls from the first to the final CEM iteration in every unit. Proposal widths contract and fitted means separate relative to the remaining search width. Pairwise ranking agreement stays near chance on Walker and declines on Cheetah; elite-set agreement does not improve. This comparison shows greater variation between scorers than between pool sources on Cheetah; Walker has variation in both and in their pairings. We use the original six units to select Random nonlinear for a one-update intervention, without inspecting intervention outcomes. Replacing its first model-ranked update with an environment-ranked update lowers final realised selected-sequence cost in those six units and in six further units held out from the selection.
| Comments: | 20 pages, including 12 pages appendix |
| Subjects: | Machine Learning (cs.LG); Robotics (cs.RO) |
| Cite as: | arXiv:2610.00921 [cs.LG] |
| (or arXiv:2610.00921v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00921 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Oliver Obst [view email]
[v1]
Thu, 1 Oct 2026 01:57:24 UTC (167 KB)
来源：arXiv：cs.LG · arxiv.org