arXiv (All)
open access
Bellman Error Minimization Via Linear Programming Normalization
This paper proposes a new functional approximation approach to reduce Bellman error in high-dimensional dynamic programming and Reinforcement Learning problems. Using a classic dynamic programming problem (network capacity control in revenue management) as the motivational example, the paper illustr
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
- Learning Capacity Constraints with Convex Neural Networks in Lot-Sizing Problems#1235837
- Endogenous Interface Pricing for Consistent Transmission–Distribution Co-Optimization With Discrete Distribution Controls#624372
- Optimization methods for reinforcement learning: theory and applications#211648
- Vector Bellman Theory for Multichain Robust Average-Reward Markov Decision Processes#1076448
- Sum Rate Maximization for the Constant Envelope MIMO Downlink / Ferhad Askerbeyli ; Gutachter: Josef A. Nossek, Amine Mezghani, Klaus Diepold ; Betreuer: Josef A. Nossek#1145484
Record · ID 1187044
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