# Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization

## TL;DR

This paper introduces advanced Bayesian optimization techniques, including Repeat Sampling and Hybrid Repeat/Multi-point Sampling, to improve the training of AI decision-makers in simulated combat scenarios with volatile objective functions.

## Contribution

It develops novel sampling methods that enhance Gaussian Process surrogate models for more reliable optimization in volatile simulation environments.

## Key findings

- HRMS improves surrogate model accuracy
- Enhanced prediction of AI performance in simulations
- More efficient parameter tuning for AI agents

## Abstract

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.