Building Better Environments for Autonomous Cyber Defence

TL;DR

This paper presents a framework and guidelines for designing effective reinforcement learning environments for autonomous cyber defense, based on expert insights from a multidisciplinary workshop.

Contribution

It introduces a decomposition framework for RL cyber environments and offers best practice guidelines for environment development and agent evaluation.

Findings

A comprehensive framework for RL environment design in cyber defense.

Best practices for building and evaluating RL agents in network security scenarios.

Insights from multidisciplinary experts on common hazards and domain knowledge.

Abstract

In November 2025, the authors ran a workshop on the topic of what makes a good reinforcement learning (RL) environment for autonomous cyber defence (ACD). This paper details the knowledge shared by participants both during the workshop and shortly afterwards by contributing herein. The workshop participants come from academia, industry, and government, and have extensive hands-on experience designing and working with RL and cyber environments. While there is now a sizeable body of literature describing work in RL for ACD, there is nevertheless a great deal of tradecraft, domain knowledge, and common hazards which are not detailed comprehensively in a single resource. With a specific focus on building better environments to train and evaluate autonomous RL agents in network defence scenarios, including government and critical infrastructure networks, the contributions of this work are twofold: (1) a framework for decomposing the interface between RL cyber environments and real systems, and (2) guidelines on current best practice for RL-based ACD environment development and agent evaluation, based on the key findings from our workshop.