  We propose a reinforcement learning framework for discrete environments in
which an agent makes both strategic and tactical decisions. The former
manifests itself through the use of value function, while the latter is powered
by a tree search planner. These tools complement each other. The planning
module performs a local \textit{what-if} analysis, which allows to avoid
tactical pitfalls and boost backups of the value function. The value function,
being global in nature, compensates for inherent locality of the planner. In
order to further solidify this synergy, we introduce an exploration mechanism
with two distinctive components: uncertainty modelling and risk measurement. To
model the uncertainty we use value function ensembles, and to reflect risk we
use propose several functionals that summarize the implied by the ensemble. We
show that our method performs well on hard exploration environments: Deep-sea,
toy Montezuma's Revenge, and Sokoban. In all the cases, we obtain speed-up in
learning and boost in performance.
