  Reinforcement Learning (RL) is a widely employed machine learning
architecture that has been applied to a variety of control problems. However,
applications in safety-critical domains require a systematic and formal
approach to specifying requirements as tasks or goals. We propose a model-free
RL algorithm that enables the use of Linear Temporal Logic (LTL) to formulate a
goal for unknown continuous-state/action Markov Decision Processes (MDPs). The
given LTL property is translated into a Limit-Deterministic Generalised Buchi
Automaton (LDGBA), which is then used to shape a synchronous reward function
on-the-fly. Under certain assumptions, the algorithm is guaranteed to
synthesise a control policy whose traces satisfy the LTL specification with
maximal probability.
