Scikit-Decide AI Framework for RL, Auto Planning and Scheduling
Scikit-decide is an AI framework for Reinforcement Learning, Automated Planning and Scheduling. This framework was initiated at Airbus (opens new window) AI Research and notably received contributions through the ANITI (opens new window) and TUPLES (opens new window) projects, and also from ANU (opens new window). TIP Please refer to the Guide and Reference sections at the top to learn how to use scikit-decide. # Main features Problem solving: describe your decision-making problem once and auto-match compatible solvers.
- ▪Scikit-decide is an AI framework for Reinforcement Learning, Automated Planning and Scheduling.
- ▪This framework was initiated at Airbus (opens new window) AI Research and notably received contributions through the ANITI (opens new window) and TUPLES (opens new window) projects, and also from ANU (opens new window).
- ▪TIP Please refer to the Guide and Reference sections at the top to learn how to use scikit-decide. # Main features Problem solving: describe your decision-making problem once and auto-match compatible solvers.
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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 | Github |
| Canonical URL | https://airbus.github.io/scikit-decide/ |
| Publication time | Wed, 16 Sep 2026 09:26:41 +0000 |
| Retrieval time | 2026-09-16T09:38:41.642Z |
| Last seen | 2026-09-16T09:38:41.642Z |
| 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 | Ku_pzS11ckOl · 1 stories |
| 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
Scikit-decide AI framework for Reinforcement Learning, Automated Planning and Scheduling Get Started → Problem solving Describe your decision-making problem once and auto-match compatible solvers.Growing catalog Enjoy a growing list of domains & solvers catalog, supported by the community.Open & Extensible Scikit-decide is open source and is able to wrap existing state-of-the-art domains/solvers. # Welcome to scikit-decide # What is it? Scikit-decide is an AI framework for Reinforcement Learning, Automated Planning and Scheduling. This framework was initiated at Airbus (opens new window) AI Research and notably received contributions through the ANITI (opens new window) and TUPLES (opens new window) projects, and also from ANU (opens new window).
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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