arXiv (All)
open access
CyberWorld: World Models for Sample-Efficient Autonomous Cyber Defense
Deep reinforcement learning has become a prominent approach to autonomous cyber defense. Existing methods are predominantly model-free and consequently require extensive environment interaction. World models provide an alternative by learning predictive dynamics and optimizing policies through imagi
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
- Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution#1155525
- ARS: Agentic Reward System for Robot Learning#1128753
- SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems#798549
- SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems#797136
- Apprenticeship Learning for Model Parameters of Partially Observable Environments#478221
Record · ID 1115849
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