  Designing reliable decision strategies for autonomous urban driving is
challenging. Reinforcement learning (RL) has been used to automatically derive
suitable behavior in uncertain environments, but it does not provide any
guarantee on the performance of the resulting policy. We propose a generic
approach to enforce probabilistic guarantees on an RL agent. An exploration
strategy is derived prior to training that constrains the agent to choose among
actions that satisfy a desired probabilistic specification expressed with
linear temporal logic (LTL). Reducing the search space to policies satisfying
the LTL formula helps training and simplifies reward design. This paper
outlines a case study of an intersection scenario involving multiple traffic
participants. The resulting policy outperforms a rule-based heuristic approach
in terms of efficiency while exhibiting strong guarantees on safety.
