MAPLE: Multi-State Aggregated Policy Evaluation for AlphaZero in Imperfect-Information Games
The paper introduces MAPLE, a new method for evaluating policies in imperfect-information games using a tree search approach. This method combines the strengths of existing techniques while controlling computational costs. Experiments demonstrate that MAPLE significantly improves performance over the traditional AlphaZero baseline in specific games.
- ▪MAPLE stands for Multi-State Aggregated Policy Evaluation.
- ▪It aggregates policy and value evaluations from multiple sampled world states within a single search tree.
- ▪Experiments on Phantom Go and Dark Hex show Elo improvements of 291 and 136, respectively.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24139 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | waT1Rpyu7zFo |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.24139 (cs) [Submitted on 22 May 2026] Title:MAPLE: Multi-State Aggregated Policy Evaluation for AlphaZero in Imperfect-Information Games Authors:Qian-Rong Li, Hung Guei, I-Chen Wu, Ti-Rong Wu View a PDF of the paper titled MAPLE: Multi-State Aggregated Policy Evaluation for AlphaZero in Imperfect-Information Games, by Qian-Rong Li and 3 other authors View PDF Abstract:Imperfect-information games (IIGs) are challenging, as players must make decisions without fully observing the true game state. While AlphaZero has achieved remarkable success in perfect-information games, extending it to IIGs remains difficult.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
