arXiv (All)
open access
Initialization-driven neural generation and training for high-dimensional optimal control and first-order mean field games
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
- Initialization-driven neural generation and training for high-dimensional optimal control and first-order mean field games#674198
- A Sample-Wise Adjoint Regression Framework for Mean-Field Control with Connections to Adjoint Matching#793627
- A Sample-Wise Adjoint Regression Framework for Mean-Field Control with Connections to Adjoint Matching#789623
- On the Generalized Conditional Gradient Method for Mean Field Games with Local Coupling Terms#998090
- Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data#973233
Record · ID 677395
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