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

2025-08-19

AI Summary

In autonomous cyber operations (ACO), reinforcement learning (RL) agents trained from scratch suffer from slow convergence and poor initial policy performance. To address this, this paper introduces, for the first time, teacher-guided learning into ACO and systematically evaluates four guidance paradigms—behavioral cloning, offline RL, curriculum learning, and reward shaping—within the CybORG simulation environment. Experimental results demonstrate that teacher guidance significantly improves early-decision quality (by 42% on average), accelerates convergence (reducing training steps by ~35%), and enhances policy robustness. The study empirically validates the efficacy of knowledge transfer in dynamic cybersecurity decision-making and establishes a reproducible methodological framework for efficient, trustworthy training of ACO agents.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• Improving slow convergence in autonomous cyber reinforcement learning
• Enhancing early-stage performance of cybersecurity decision agents
• Applying teacher-guided techniques to autonomous cyber operations

Innovation

• Teacher-guided reinforcement learning techniques
• Simulated CybORG environment implementation
• Improved training efficiency and convergence