In this project, we investigate the application of Gaussian process models for decision-making under uncertainty. We consider the setting where the true parameters of the Gaussian process must be inferred whilst simultaneously identifying the maxima of the Gaussian process through noisy observations. The project intends to develop novel algorithms for this problem and provide experimental validation of their performance.