Odalric-Ambrym Maillard

Information-Based Exploration via Random Features for Reinforcement Learning

Random Feature Information Gain is introduced, grounded in Bayesian kernel methods theory, which uses random Fourier features to approximate information gain and compute exploration bonuses in non-countable spaces, and provides error bounds on information gain approximation.