Safe Reinforcement Learning on Autonomous Vehicles

-- There have been numerous advances in reinforcement learning, but the typically unconstrained exploration of the learning process prevents the adoption of these methods in many safety critical applications. Recent work in safe reinforcement learning uses idealized models to achieve their guarantees, but these models do not easily accommodate the stochasticity or high-dimensionality of real world systems. We investigate how prediction provides a general and intuitive framework to constraint exploration, and show how it can be used to safely learn intersection handling behaviors on an autonomous vehicle. I. INTRODUCTION With the increasing complexity of robotic systems, and the continued advances in machine learning, it can be tempting to apply reinforcement learning (RL) to challenging control problems. However the trial and error searches typical to RL methods are not appropriate to physical systems which act in the real world where failure cases result in real consequences. To mitigate the safety concerns associated with training an RL agent, there have been various efforts at designing learning processes with safe exploration. As noted by Garcia and Fernandez [1], these approaches can be broadly classified into approaches that modify the objective function and approaches that constrain the search space. Modifying the objective function mostly focuses on catastrophic rare events which do not necessarily have a large impact on the expected return over many trials. Proposed methods take into account the variance of return [2], the worst-outcome [3], [2], [4], and the probability of visiting error states [5].