Bayesian policy gradient and actor-critic algorithms
The research introduces Bayesian Policy Gradient (BPG) using Gaussian Processes to estimate policy gradients, significantly reducing sample complexity and quantifying gradient uncertainty via covariance. It further develops a Bayesian Actor-Critic (BAC) algorithm that leverages Gaussian Process Temporal Difference Learning to better handle Markovian systems, resulting in more accurate gradient estimates than standard methods.