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

arXiv: https://arxiv.org/abs/2508.14340 · HTML: https://mlsanity.com/paper/2508.14340/

## 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.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations](https://mlsanity.com/paper/1805.07095/) (2018)
- [CybORG: An Autonomous Cyber Operations Research Gym](https://mlsanity.com/paper/2002.10667/) (2020)
- [Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.05939/) (2023)
- [Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence](https://mlsanity.com/paper/2507.14658/) (2025)

## Built on by (most cited first, 2 of 2)

- [Large Language Model Integration with Reinforcement Learning to Augment Decision-Making in Autonomous Cyber Operations](https://mlsanity.com/paper/2509.05311/) (2025)
- [Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations](https://mlsanity.com/paper/2607.28826/) (2026)

## 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.