自动驾驶中强化学习算法的综合比较
A Comprehensive Comparison of Reinforcement Learning Algorithms in Autonomous Driving
Dechen Xie, Mushuang Liu
AI 速读 基于摘要
在同一无信号交叉口自动驾驶决策问题上，对FVI、RCPI、DQN和D-DDPG四种强化学习方法进行最优性、安全性、训练稳定性与成本比较。
cs.LG · eess.SY
查看原文摘要
This paper presents a comprehensive comparison of four representative Reinforcement Learning (RL) methods in autonomous vehicle (AV) decision-making, including Fitted Value Iteration (FVI), Rollout Classification Policy Iteration (RCPI), Deep Q-Network (DQN), and a discrete-action adaptation of Deep Deterministic Policy Gradient (D-DDPG). The four methods are applied to the same AV decision-making problem at an unsignalized intersection and are compared in terms of optimality, safety, training stability, and training and decision cost. Observations suggest that RCPI provides the closest estimation to the exact solution and has the most stable training curve; however, it is data-intensive and requires the largest volume of data. D-DDPG is the most data-efficient one, but at the price of the training stability. Based on the observations, we provide suggestions for the algorithm selection for AV decision making.