arXiv (OAI Expanded)
open access
Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs
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
- Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs#612830
- Learning Chance-Constrained MDPs with Bellman Distributional Certificates#1105415
- No good Markov strategies for Büchi objectives in countable MDPs.#244273
- No good Markov strategies for Büchi objectives in countable MDPs#240981
- Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making#684438
Record · ID 611353
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