Generates diverse, high‑impact perturbations in silico, allowing early tuning of curriculum parameters and detection thresholds before real‑world adversarial testing.
What Is Modelled
The interaction between a multi‑agent reinforcement learning policy and a curriculum of LLM‑generated semantic adversarial scenarios, measuring the resulting policy regret and identifying safe curriculum parameters.
Objectives
Generate a diverse set of semantic adversarial scenarios using a large language model.
Simulate multi‑agent RL agents under these scenarios and compute policy regret.
Use a hyper‑heuristic to search curriculum parameter space and identify configurations that minimise regret.
Produce safety thresholds and curriculum design guidelines for deployment.
Validate simulation results against real‑world logs from earlier chapters.
Success Criteria
Achieve >95% coverage of known adversarial prompt types in the generated corpus.
Policy regret metric converges within 10% of ground‑truth values from pilot data.
Hyper‑heuristic identifies a curriculum that reduces regret by ≥30% compared to baseline.
Average simulation runtime per iteration < 2 h on a GPU cluster.
Deliver a parameter–regret surface with 95% confidence intervals.
Output Form
CSV tables of curriculum parameters vs. regret, JSON safety‑threshold spec, and parameter–regret surface plots (Matplotlib/Seaborn).
Key Parameters & What They Affect
Parameter
Range / Units
Affects
Notes
curriculum_depth
1–10 (integer)
policy regrettraining time
Number of successive adversarial rounds per episode.
semantic_shift_intensity
0.0–1.0 (float)
policy regretmodel interpretability
Degree of semantic distortion applied to prompts.
adversarial_agent_count
1–5 (integer)
policy regretcommunication overhead
Number of agents generating adversarial messages.
policy_update_frequency
10–100 steps (integer)
policy stabilityregret convergence
How often the policy is updated during a scenario.
reward_penalty_weight
0.0–5.0 (float)
policy regretexploration
Weight applied to penalty for mis‑aligned actions.
Input Data
Required data:
RL environment definition (e.g., SMAC or custom grid world)
Baseline policy weights (pre‑trained)
LLM inference credentials (OpenAI API key or local Llama 3)
Optional adversarial prompt dataset (e.g., D‑REX)
Natural Sources (from the project)
Interaction logs from Chapter 1 test rigs (AOI‑GBE logs)
Policy performance logs from prior simulation runs
Acquired Sources
OpenAI Prompt Injection dataset (public)
OpenAI GPT‑4 API
HuggingFace Llama 3 70B model
OpenAI Gym wrappers for multi‑agent environments
Synthesised Sources
LLM‑generated adversarial prompts using controlled templates
Diffusion‑based text perturbations for semantic shift
Monte Carlo synthetic scenario generator seeded with domain knowledge
Engineer / Scientist Guidance
Set up a Docker image with Python 3.11, PyTorch 2.0, and Ray 2.0.
Install OpenAI SDK or local Llama 3 inference server (vllm).
Define the RL environment (SMAC or custom) and load baseline policy weights.
Implement the LLM‑AC generator: a function that takes curriculum parameters and outputs a list of adversarial prompts.
Wrap the RL loop: for each episode, inject generated prompts, run the policy, and record cumulative reward and regret.
Compute policy regret as the difference between expected reward under clean scenarios and reward under adversarial scenarios.
Create an Optuna study with a Bayesian sampler to explore the 5‑dimensional curriculum space.
In the objective function, launch a lightweight RL training run (e.g., 500 episodes) and return the mean regret.
Set Optuna to run 200 trials with a 2‑hour timeout per trial on a GPU node.
After the study, extract the best parameter set, generate a parameter–regret surface, and export safety thresholds.
Validate the simulation results against pilot logs from Chapter 1 using a paired t‑test (p < 0.05).
Document the entire pipeline in a GitHub repository with CI/CD via GitHub Actions.
Recommended Tools
OpenAI GPT‑4 / Llama 3 (vllm)Ray RLlibOptunaSimPy (for Monte Carlo scenario generation)Python 3.11PyTorch 2.0NumPyPandasMatplotlibSeabornDockerGitHub Actions
Validation & Verification
Compare the mean policy regret from simulation to the regret observed in the 4‑week UAV swarm pilot (Chapter 1). Perform a paired t‑test across 30 random seeds; accept if p < 0.05. Additionally, run a 5‑fold cross‑validation of the hyper‑heuristic to ensure stability of the identified curriculum parameters.
Expected Impact
Quality
Improved curriculum robustness reduces policy regret by ~30% and lowers hallucination amplification risk.
Timescale
Accelerates safety‑threshold tuning from 6 months to 2 months.
Cost
Reduces need for expensive real‑world adversarial testing by ~70%.
Risk Retired
Early detection of cascading misinterpretation and policy drift, mitigating mission failure.
Software Tool Development Prompts
Drop these into a coding assistant toscaffold the supporting software for this modelling task.
Create an Optuna study that optimizes the following curriculum parameters: curriculum_depth (1‑10), semantic_shift_intensity (0‑1), adversarial_agent_count (1‑5), policy_update_frequency (10‑100), reward_penalty_weight (0‑5). The objective function should launch a Ray RLlib training run for 500 episodes, compute average policy regret, and return it. Use a Bayesian sampler and limit each trial to 2 hours on a single GPU. Provide the full Python script.
Implement a function `generate_adversarial_prompts(llm, depth, intensity, agent_count)` that uses a large language model (OpenAI GPT‑4 or local Llama 3) to produce a list of `depth` adversarial prompts. Each prompt should apply a semantic shift controlled by `intensity` (0 = no shift, 1 = maximum distortion) and be tailored to `agent_count` agents. Return the prompts as a JSON array. Include error handling for API rate limits and a retry mechanism.
Risks & Assumptions
LLM hallucinations may produce unrealistic prompts that do not reflect real adversarial tactics.
RL training is computationally expensive; budget constraints may limit the number of hyper‑heuristic trials.
Hyper‑heuristic may converge to a local optimum; multiple restarts are recommended.
Assumes the simulation environment accurately captures real‑world dynamics; discrepancies could invalidate safety thresholds.
Policy regret is assumed to correlate with safety; additional metrics (e.g., explainability drift) may be needed.