arXiv (All)
open access
Transformation of Adaptive Multistage Sampling for Solving Finite-Horizon Markov Decision Processes with Unknown Model
This work provides a learning approach to solving finite-horizon Markov decision processes (MDPs) when the underlying model of a given finite MDP is unknown to the decision maker. We transform the adaptive multistage sampling (AMS) algorithm into a sampling-free algorithm, called "adaptive multistag
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
- Randomization for Faster Exact Optimization of Discounted Markov Decision Processes#1129289
- Sampling bias corrections for discrete and Gaussian partial information decompositions#1110855
- dOPT: Differentiating Conic Optimization via Geometric Reduction#1118901
- Nash Approximation Gap in Truncated Infinite-horizon Partially Observable Markov Games#1048624
- Explainable Representation of Finite-Memory Policies for POMDPs using Decision Trees#179093
Record · ID 973478
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