Reinforcement learning with Gaussian process regression using variational free energy

Abstract

The essential part of existing reinforcement learning algorithms that use Gaussian process regression involves a complicated online Gaussian process regression algorithm. Our study proposes online and mini-batch Gaussian process regression algorithms that are easier to implement and faster to estimate for reinforcement learning. In our algorithm, the Gaussian process regression updates the value function through only the computation of two equations, which we then use to construct reinforcement learning algorithms. Our numerical experiments show that the proposed algorithm works as well as those from previous studies.

Keywords

• reinforcement learning
• gaussian process
• q-learning
• bayesian inference
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