  We propose a hybrid approach aimed at improving the sample efficiency in
goal-directed reinforcement learning. We do this via a two-step mechanism where
firstly, we approximate a model from Model-Free reinforcement learning. Then,
we leverage this approximate model along with a notion of reachability using
Mean First Passage Times to perform Model-Based reinforcement learning. Built
on such a novel observation, we design two new algorithms - Mean First Passage
Time based Q-Learning (MFPT-Q) and Mean First Passage Time based DYNA
(MFPT-DYNA), that have been fundamentally modified from the state-of-the-art
reinforcement learning techniques. Preliminary results have shown that our
hybrid approaches converge with much fewer iterations than their corresponding
state-of-the-art counterparts and therefore requiring much fewer samples and
much fewer training trials to converge.
