Training Environments for Reinforcement Learning Cybersecurity Agents
Bibliographic record
Abstract
The development and training of Reinforcement Learning (RL) agents that can be applied to solve cybersecurity related challenges is a growing field of research. However, creating environments to train these agents effectively and efficiently is a nontrivial task. As part of the Markov Decision Process (MDP) involved in training an RL agent, the model must take actions on an environment and observe the impact of these actions on the state of the environment. For RL agents training in the field of cybersecurity, the environment being acted upon is often a computer network. Two primary streams of training environment have been developed for such agents: network emulators and network simulators. Emulators employ Virtual Machines (VMs) to provide a high-fidelity environment for the agent's interactions but suffer in efficiency since the actions of the agent are executed in real-time on the emulated network. Simulators employ abstracted models of the network environment that can rapidly compute the effect of an action on the environment. Simulated environments can train RL agents in much less time but introduce a simulation-to-reality gap that may result in an agent trained in a simulated environment that performs poorly in real environments. This paper reviews current emulated and simulated network environments for RL training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".