Safe Reinforcement Learning on Autonomous Vehicles
This addresses safety-critical applications like autonomous driving, but appears incremental as it builds on existing safe RL methods with a new framework.
The paper tackled the problem of safe reinforcement learning for autonomous vehicles by using prediction to constrain exploration, and demonstrated its application to safely learn intersection handling behaviors.
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.