Randomized Prior Functions for Deep Reinforcement Learning

Dealing with uncertainty is essential for e cient reinforcement learning. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the most popular approaches are poorlysuited to sequential decision problems. Other methods, such as bootstrap sampling, have no mechanism for uncertainty that does not come from the observed data. We highlight why this can be a crucial shortcoming and propose a simple remedy through addition of a randomized untrainable'prior' network to each ensemble member. We prove that this approach is e cient with linear representations, provide simple illustrations of its e cacy with nonlinear representations and show that this approach scales to large-scale problems far better than previous attempts.