From Imitation to Interaction: Mastering Game of Schnapsen with Shallow Reinforcement Learning
The paper explores the effectiveness of shallow neural network agents in mastering the card game Schnapsen. It compares a supervised learning agent with a reinforcement learning agent, finding that the latter significantly outperforms the former. The study concludes that reinforcement learning, particularly when combined with deeper lookahead strategies, leads to higher winning rates against strong opponents.
- ▪Shallow neural network agents were tested for their ability to master the game Schnapsen.
- ▪Supervised imitation learning did not generalize well against strong opponents, while reinforcement learning showed better results.
- ▪The best performance was achieved by combining learned value functions with deeper lookahead during gameplay.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17162 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| 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 | 2y5hSg6lEc-e |
| 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:2605.17162 (cs) [Submitted on 16 May 2026] Title:From Imitation to Interaction: Mastering Game of Schnapsen with Shallow Reinforcement Learning Authors:Ján Klačan, Sizhong Zhang View a PDF of the paper titled From Imitation to Interaction: Mastering Game of Schnapsen with Shallow Reinforcement Learning, by J\'an Kla\v{c}an and Sizhong Zhang View PDF HTML (experimental) Abstract:This paper investigates whether shallow neural network agents can master the card game Schnapsen and challenge a strong search-based baseline, RdeepBot, which uses Monte Carlo sampling and lookahead search.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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