  This paper addresses the problem of learning control policies for mobile
robots, modeled as unknown Markov Decision Processes (MDPs), that are tasked
with temporal logic missions, such as sequencing, coverage, or surveillance.
The MDP captures uncertainty in the workspace structure and the outcomes of
control decisions. The control objective is to synthesize a control policy that
maximizes the probability of accomplishing a high-level task, specified as a
Linear Temporal Logic (LTL) formula. To address this problem, we propose a
novel accelerated model-based reinforcement learning (RL) algorithm for LTL
control objectives that is capable of learning control policies significantly
faster than related approaches. Its sample-efficiency relies on biasing
exploration towards directions that may contribute to task satisfaction. This
is accomplished by leveraging an automaton representation of the LTL task as
well as a continuously learned MDP model. Finally, we provide comparative
experiments that demonstrate the sample efficiency of the proposed method
against recent RL methods for LTL objectives.
