Deeper & Sparser Exploration
We address the problem of efficient exploration by proposing a new meta algorithm in the context of model-based online planning for Bayesian Reinforcement Learning (BRL). We beat the stateof-the-art, while staying computationally faster, in some cases by two orders of magnitude. This is the first Optimism free BRL algorithm to beat all previous state-of-the-art in tabular RL. The main novelty is the use of a candidate policy generator, to generate long-term options in the belief tree, which allows us to create much sparser and deeper trees. We present results on many standard environments and empirically prove its performance.