Title: Deep Exploration via Randomized Value Functions,
Abstract: We study the use of randomized value functions to guide deep exploration in
reinforcement learning. This offers an elegant means for synthesizing
statistically and computationally efficient exploration with common practical
approaches to value function learning. We present several reinforcement
learning algorithms that leverage randomized value functions and demonstrate
their efficacy through computational studies. We also prove a regret bound that
establishes statistical efficiency with a tabular representation.