  This paper addresses the problem of designing optimal control policies for
mobile robots with mission and safety requirements specified using Linear
Temporal Logic (LTL). We consider robots with unknown stochastic dynamics
operating in environments with unknown geometric structure. The robots are
equipped with sensors allowing them to detect obstacles. Our goal is to
synthesize a control policy that maximizes the probability of satisfying an
LTL-encoded task in the presence of motion and environmental uncertainty.
Several deep reinforcement learning (DRL) algorithms have been proposed
recently to address similar problems. A common limitation in related works is
that of slow learning performance. In order to address this issue, we propose a
novel DRL algorithm, which has the capability to learn control policies at a
notably faster rate compared to similar methods. Its sample efficiency is due
to a mission-driven exploration strategy that prioritizes exploration towards
directions that may contribute to mission accomplishment. Identifying these
directions relies on an automaton representation of the LTL task as well as a
learned neural network that (partially) models the unknown system dynamics. We
provide comparative experiments demonstrating the efficiency of our algorithm
on robot navigation tasks in unknown environments.
