arXiv (All)
open access
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective
Offline model-based reinforcement learning (MBRL) serves as a competitive framework that can learn well-performing policies solely from pre-collected data with the help of learned dynamics models. To fully unleash the power of offline MBRL, model selection plays a pivotal role in determining the dyn
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
- Lifted Bellman Linear Programming for Offline Reinforcement Learning#1050662
- Lifted Bellman Linear Programming for Offline Reinforcement Learning#1028719
- Is One Step Enough for Offline Policy Improvement?#1319157
- Multi-step Proximal Policy Improvement in Offline Reinforcement Learning#639687
Record · ID 1389061
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