mlsanityData to 14 Oct 2026

A Comparative Evaluation of Teacher-Guided Reinforcement Learning Techniques for Autonomous Cyber Operations

arXiv 2508.14340 ↗First posted 20 Aug 2025cs.LG cs.AIcited by 2

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

Autonomous Cyber Operations (ACO) rely on Reinforcement Learning (RL) to train agents to make effective decisions in the cybersecurity domain. However, existing ACO applications require agents to learn from scratch, leading to slow convergence and poor early-stage performance. While teacher-guided techniques have demonstrated promise in other domains, they have not yet been applied to ACO. In this study, we implement four distinct teacher-guided techniques in the simulated CybORG environment and conduct a comparative evaluation. Our results demonstrate that teacher integration can significantly improve training efficiency in terms of early policy performance and convergence speed, highlighting its potential benefits for autonomous cybersecurity.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 14 references that are in the map. First posted on arXiv 20 Aug 2025.

Builds on

6 of 14 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2018Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrationscited by 38
  4. 2020CybORG: An Autonomous Cyber Operations Research Gymcited by 16
  5. 2023Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learningcited by 12
  6. 2025Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defencecited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Large Language Model Integration with Reinforcement Learning to Augment Decision-Making in Autonomous Cyber Operationscited by 4
  2. 2026Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operationscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Towards Production-Worthy Simulation for Autonomous Cyber Operations
  2. 2021CybORG: A Gym for the Development of Autonomous Cyber Agents
  3. 2024Exploring reinforcement learning for incident response in autonomous military vehicles
  4. 2026Building Better Environments for Autonomous Cyber Defence
  5. 2024Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense
  6. 2026Learning Communication Between Heterogeneous Agents in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
  7. 2026Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents
  8. 2025Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications