予測を活用した自動運転車の安全強化学習
Safe Reinforcement Learning on Autonomous Vehicles
予測を用いて探索を制約する枠組みを提案し、自動運転車が交差点での運転行動を安全に学習できることを示した。
著者: David Isele, Alireza Nakhaei, Kikuo Fujimura
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
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.
