arXiv (All)
open access
Test-time Reinforcement Learning in Imperfect Information Games
Test-time reasoning has significantly improved performance in domains ranging from games to language models. However, test-time policy changes with formal guarantees on the performance of the resulting strategy remain a challenge in two-player zero-sum imperfect-information games. Existing solutions
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Test-time Reinforcement Learning in Imperfect Information Games#808702
- Explaining Reinforcement Learning Decisions in Self-adaptive Systems#609058
- JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces#1155372
- Mice plan decision strategies based on previously learned time intervals, locations, and probabilities#934587
- Truth Bias in Human Reinforcement Learning#1148341
Record · ID 818694
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive