arXiv CS
open access
Ask the Expert: LLM-Guided Reinforcement Learning for Autonomous Cyber Defense
Policy-based reinforcement learning (RL) approaches have produced promising results for autonomous cyber defense; however, they are sample-inefficient in settings where defenders must respond under delayed, partial observations with actions from large action spaces. While large language models (LLMs
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
- Ask the Expert: LLM-Guided Reinforcement Learning for Autonomous Cyber Defense#1233376
- Privacy-Preserving Prompted Policy Search for Robotic Control#1104753
- CyberWorld: World Models for Sample-Efficient Autonomous Cyber Defense#1115849
- ARS: Agentic Reward System for Robot Learning#1128753
- Value-Preserving Action Quantization#1342128
Record · ID 1227271
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