arXiv (All)
open access
Last-Iterate Guarantees for Online Reinforcement Learning in Structured Constrained MDPs
In safety-critical applications, deployment uses a single policy, whose performance and constraint satisfaction should hold directly rather than only for an average or mixture of training policies. This motivates last-iterate guarantees in constrained reinforcement learning. Recent progress has esta
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 Chance-Constrained MDPs with Bellman Distributional Certificates#1105415
- Provably Optimal Reinforcement Learning under Safety Filtering#1034680
- SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking#973670
- Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation#1104441
- Last-Iterate Convergence of General Parameterized Policies in Constrained MDPs#189207
Record · ID 1117645
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