  This study provides both analysis and a refined, research-ready
implementation of Tang and Kucukelbir's Variational Deep Q Network, a novel
approach to maximising the efficiency of exploration in complex learning
environments using Variational Bayesian Inference. Alongside reference
implementations of both Traditional and Double Deep Q Networks, a small novel
contribution is presented - the Double Variational Deep Q Network, which
incorporates improvements to increase the stability and robustness of
inference-based learning. Finally, an evaluation and discussion of the
effectiveness of these approaches is discussed in the wider context of Bayesian
Deep Learning.
