Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization
This work addresses the challenge of optimizing AI agents in volatile simulation environments like dog-fighting, though it is incremental as it builds on existing Bayesian optimization methods.
The paper tackles the problem of training AI decision-makers in aerial combat by optimizing tunable parameters against an intelligent adversary, using novel sampling techniques to improve Gaussian process Bayesian optimization, resulting in more accurate performance predictions and efficient parameter tuning as shown in simulation studies.
This work studies how an AI-controlled dog-fighting agent with tunable decision-making parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are developed to automatically learn global Gaussian Process (GP) surrogate models, which provide statistical performance predictions in both explored and unexplored areas of the parameter space. This allows a learning engine to sample full-combat simulations at parameter values that are most likely to optimize performance and also provide highly informative data points for improving future predictions. However, standard GPBO methods do not provide a reliable surrogate model for the highly volatile objective functions found in aerial combat, and thus do not reliably identify global maxima. These issues are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multi-point Sampling (HRMS) techniques. Simulation studies show that HRMS improves the accuracy of GP surrogate models, allowing AI decision-makers to more accurately predict performance and efficiently tune parameters.