ISTA Publications
metadata only
Lower bound on Howard policy iteration for deterministic Markov Decision Processes
Deterministic Markov Decision Processes (DMDPs) are a mathematical framework for decision-making where the outcomes and future possible actions are deterministically determined by the current action taken. DMDPs can be viewed as a finite directed weighted graph, where in each step, the controller ch
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
- On the Termination Problem for Probabilistic Higher-Order Recursive Programs#635508
- On the Termination Problem for Probabilistic Higher-Order Recursive Programs#633159
- Revealing POMDPs: Qualitative and quantitative analysis for parity objectives#1134268
- Model-Based Learning of Near-Optimal Finite-Window Policies in POMDPs#670543
- Model-Based Learning of Near-Optimal Finite-Window Policies in POMDPs#668478
Record · ID 1066570
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