arXiv (OAI Expanded)
open access
Safe Meta-Reinforcement Learning via Information Space Reachability
Meta-reinforcement learning (meta-RL) enables agents to adapt to unseen tasks with limited experience. Despite its promise, the application of meta-RL in real-world tasks is hindered by safety requirements, which have been underexplored in prior work. In this paper, we propose a safe meta-RL framewo
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
- Unlearnable, or Unmeasured? On the Reliability of Difficulty Labels in RLVR#1154382
- Unlearnable, or Unmeasured? On the Reliability of Difficulty Labels in RLVR#1136873
- Adapter Thickets: Splitting an RLVR Budget Beats Concentrating It#1155926
- The hard problem of meta-learning is what-to-learn#87340
- Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale#1142682
Record · ID 1109949
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