Dynamic, data‑driven selection of low‑level search heuristics to accelerate robust policy inference under adversarial observation perturbations.
Hyper‑heuristicReinforcement LearningBayesian OptimizationFeasibilitydepends on #1: Synthetic Adversarial Observation Perturbation Dataset Generationdepends on #2: Bayesian Policy Inference Simulationdepends on #3: LLM-driven Adversarial Curriculum Simulation
Source in Roadmap / Ideate
Chapter 1 – AOI‑GBE Hyper‑heuristic Layer
Why model first
Provides a dynamic, data‑driven way to navigate the large configuration space, improving convergence and reducing manual tuning before hardware integration.
What Is Modelled
The AOI‑GBE framework for multi‑agent policy inference, comprising conditional GAN observation modeling (CC‑GAN), Bayesian policy inference (BPI), LLM‑driven adversarial curriculum (LLM‑AC), cooperative resilience layer (CRL), meta‑learning inference‑time adaptation (ML‑ITA), and explainable inference traces (EIT).
Objectives
Select and adapt low‑level heuristics (GAN schedules, Bayesian inference strategies, meta‑learning update rates) online to maximize policy performance under unseen observation perturbations.
Reduce overall training time and GPU cost by 30% compared to manual tuning.
Maintain or improve detection F1 > 0.80, recovery latency < 200 ms, and explainability fidelity > 0.85.
Provide a reproducible, automated pipeline that can be warm‑started on related AOI‑GBE instances.
Success Criteria
AOI‑GBE trained with hyper‑heuristic achieves ≥95% of the best manual‑tuned configuration on a held‑out adversarial benchmark.
Total GPU hours spent on hyper‑heuristic search ≤ 200 h for a 10‑iteration run.
The selected configuration is stable (variance < 5%) across 5 random seeds.
All metrics (reward, detection, recovery, explainability) meet or exceed the baseline values defined in the AOI‑GBE feasibility report.
Output Form
A JSON manifest of the best hyper‑heuristic configuration, a trained AOI‑GBE model checkpoint, and a CSV of evaluation metrics for each candidate. The manifest includes: GAN optimizer, learning rate, batch size, epochs; Bayesian prior type, inference method, network depth; meta‑learning step size, inner‑loop count; curriculum difficulty schedule; and EIT saliency threshold.
Key Parameters & What They Affect
Parameter
Range / Units
Affects
Notes
GAN_optimizer
['Adam', 'RMSprop', 'SGD']
speedquality
Controls convergence stability of CC‑GAN.
GAN_learning_rate
1e-5 to 1e-3
speedquality
Higher LR speeds training but may cause mode collapse.
GAN_batch_size
32, 64, 128
speedquality
Balances GPU memory vs. gradient noise.
GAN_epochs
50 to 200
speedquality
Trade‑off between reconstruction fidelity and training time.
Bayesian_prior
['Gaussian', 'Laplace', 'Hierarchical']
qualityreliability
Affects posterior calibration of policies.
Inference_method
['Variational', 'MCMC', 'Amortized']
speedquality
Determines marginalization cost.
Meta_step_size
1e-4 to 1e-2
speedquality
Controls adaptation speed of CC‑GAN during inference.
Meta_inner_loops
1 to 5
speedquality
Number of gradient steps for MAML‑style fine‑tuning.
Curriculum_difficulty
['Low', 'Medium', 'High']
qualitysample efficiency
Defines the severity of LLM‑generated adversarial scenarios.
EIT_saliency_threshold
0.1 to 0.5
explainability
Threshold for flagging latent dimensions as influential.
Input Data
Required data:
Interaction logs with nominal and adversarial observations (timestamped sensor streams, action labels, rewards).
Synthetic adversarial logs generated by rule‑based and LLM‑based curricula.
Pre‑trained embeddings for LLM‑AC (e.g., GPT‑4 embeddings).
Ground‑truth policy labels for evaluation (if available).
Natural Sources (from the project)
Phase 1 dataset repository (Chapter 1, Foundations & Data Collection).
On‑board telemetry from UAV swarm testbed (Phase 1, Deploy Sensor Suite & Logging).
Acquired Sources
Public UAV telemetry datasets (e.g., OpenPilot, PX4 logs).
Adversarial example libraries (e.g., AutoAttack, PGD).
GAN synthetic observation samples for pre‑training.
Engineer / Scientist Guidance
Set up a shared experiment repository (GitHub + DVC) and create a baseline AOI‑GBE training script that accepts hyper‑parameter JSON.
Install Optuna (or Ray Tune) and define a study with a mixed search space covering the key parameters listed above.
Wrap the training script in a callable objective function that: (a) loads the dataset, (b) configures CC‑GAN, BPI, LLM‑AC, CRL, ML‑ITA, EIT according to the trial parameters, (c) trains for a fixed budget (e.g., 2 GPU‑hours), and (d) returns a composite score = 0.4*reward + 0.3*detF1 + 0.2*recovLatency + 0.1*explainFidelity.
Enable early‑stopping inside the objective: if validation reward stagnates for 3 epochs, abort the trial to conserve resources.
After each trial, log metrics to MLflow (or Weights & Biases) and store the checkpoint if it beats the current best.
Run the study for 500 trials or until the best score plateaus (<1% improvement over last 20 trials).
Once the study finishes, extract the best trial configuration, freeze the hyper‑parameters, and re‑train AOI‑GBE to convergence (e.g., 200 epochs) for final evaluation.
Validate the final model on a held‑out adversarial benchmark (e.g., AutoAttack + LLM‑AC generated scenarios) and compare against the manual‑tuned baseline.
Generate a reproducible Docker image containing the trained model, inference script, and a lightweight API (FastAPI) for downstream agents.
Document the hyper‑heuristic pipeline in a Jupyter notebook, including code snippets, parameter ranges, and visualizations of the search trajectory.
Recommended Tools
Optuna (Python) for hyper‑heuristic orchestrationRay Tune (Python) for distributed trial executionPyTorch Lightning for modular training of CC‑GAN and BPITensorFlow/Keras for LLM‑AC prompt generation (via HuggingFace Transformers)scikit‑learn for Bayesian inference utilities (GaussianProcessRegressor, MCMC)MLflow or Weights & Biases for experiment trackingFastAPI for inference APIDocker for containerizationKubernetes (minikube) for local orchestrationBoTorch + Ax for Bayesian optimization over discrete + continuous spaces
Validation & Verification
The final AOI‑GBE model will be validated against a two‑tier test: (1) a synthetic adversarial benchmark (AutoAttack + LLM‑AC generated scenarios) to measure reward, detection F1, recovery latency, and explainability fidelity; (2) a real‑world UAV swarm deployment (10 agents) for 4 weeks to assess mission success rate and operator trust scores. Cross‑validation will be performed across 5 random seeds. The hyper‑heuristic pipeline will be audited by a third‑party to ensure reproducibility of the best configuration.
Expected Impact
Quality
Improved policy robustness (≥10% higher reward under 30% observation corruption) and better explainability (saliency fidelity ↑).
Timescale
Reduces manual tuning cycle from 3 months to 6 weeks; overall AOI‑GBE deployment accelerated by 25%.
Cost
GPU hours cut by ~30% and manual labor reduced by ~40%.
Risk Retired
Mitigates risk of over‑fitting to a single heuristic, reduces chance of catastrophic failure due to unseen adversarial tactics.
Software Tool Development Prompts
Drop these into a coding assistant toscaffold the supporting software for this modelling task.
Create a Python script that uses Optuna to orchestrate a hyper‑heuristic search over the AOI‑GBE configuration space. The script should define a mixed search space (categorical, float, integer), wrap the AOI‑GBE training pipeline in an objective function, implement early‑stopping, log metrics to MLflow, and output the best trial configuration as a JSON file.
Write a data preprocessor in Python that takes raw UAV telemetry logs (CSV) and produces two datasets: (a) nominal observations and (b) adversarially perturbed observations generated by a rule‑based noise engine. The preprocessor should output a TFRecord file for each dataset, include a split into train/val/test (70/15/15), and store metadata (sensor schema, perturbation type) in a JSON sidecar.
Risks & Assumptions
Assumption: Sufficient GPU resources (≥8× NVIDIA RTX 3090) are available for parallel trial execution.
Risk: GAN mode collapse may occur for certain hyper‑parameter combinations; early‑stopping mitigates wasted compute but may bias the search.
Risk: Bayesian inference methods (MCMC) can be too slow for the evaluation budget; fallback to variational inference is required.
Assumption: LLM‑AC prompt generation is deterministic enough to produce reproducible curricula; otherwise, seed control is necessary.
Risk: The composite objective may overweight reward at the expense of explainability; careful weighting and sensitivity analysis are required.
Risk: Data drift between synthetic and real UAV logs could reduce transferability; continuous monitoring of drift metrics is recommended.