arXiv (All)
open access
DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management
Deep Reinforcement Learning (DRL) provides a general-purpose methodology for training inventory policies that can leverage big data and compute. However, off-the-shelf implementations of DRL have seen mixed success, often plagued by high sensitivity to the hyperparameters used during training. In th
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
- REFINEPPO: Learning Continuous Control Policies by Iterative Action Refinement#1035078
- Composite-Gradient Learning for Shared Control Authority Between Deep Reinforcement Learning and Model Predictive Control#1011422
- Learning When to Refine: Long-Horizon Reinforcement Learning for Budgeted Neural-Operator PDE Solvers#1215965
- AI-Powered Scheduling in High-Mix, Low-Volume (HMLV) Manufacturing: An Appraisal of the Challenges, Approaches, and Future Directions#713905
- Decision Titan: Test-Time Training for Long-Term Memory in Offline Reinforcement Learning#1156785
Record · ID 1200884
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