arXiv (All)
open access
Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation
We study value adaptation in offline-to-online reinforcement learning under general function approximation. Starting from an imperfect offline pretrained $Q$-function, the learner aims to adapt it to the target environment using only a limited amount of online interaction. We first characterize the
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
- Active Offline-to-Online Reinforcement Learning#363249
- Official Review · ICLR 2025#787018
- RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking#1050876
- Offline Policy Evaluation via Mixed Bellman Residuals and Adaptive Critic Representations#1117898
- Preserve Support, Not Correspondence: Dynamic Routing for Offline Reinforcement Learning#134607
Record · ID 1247371
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