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- Title
基于PPO 算法的自动驾驶 人机交互式强化学习方法.
- Authors
时高松; 赵清海; 董鑫; 贺家豪; 刘佳源
- Abstract
To address the high computational demands and slow convergence faced by DRL in the field of autonomous driving, this paper integrated VAE with PPO algorithm. By adopting VAE's feature encoding technology, it effectively transformed semantic images obtained from the Carla simulator into state inputs, thus tackling the high computational load of DRL in handling complex autonomous driving tasks. To solve the issues of local optima and slow convergence in DRL training, it introduced a driving intervention mechanism and a driver-guided experience replay mechanism. These mechanisms applied driving interventions during the initial training phase and when the model encounters local optima, so as to enhance the model's learning efficiency and generalization capability. Experimental validation, conducted in left-turn scenarios at intersections, shows that with the aid of the driving intervention mechanism, the model's performance improves more rapidly in the initial training phase. Moreover, driving interventions when encountering local optima further enhance the model's performance, with even more significant improvements observed in complex scenarios.
- Subjects
DEEP reinforcement learning; REINFORCEMENT learning; TRAFFIC safety; AUTONOMOUS vehicles; LOCAL government
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2024, Vol 41, Issue 9, p2732
- ISSN
1001-3695
- Publication type
Article
- DOI
10.19734/j.issn.1001-3695.2024.01.0018