Data Efficient Reinforcement Learning for Integrated Lateral Planning and Control in Automated Parking System.

ABSTRACT: Reinforcement learning (RL) is a promising direction in automated parking systems (APSs), as integrating planning and tracking control using RL can potentially maximize the overall performance. However, commonly used model-free RL requires many interactions to achieve acceptable performance, and model-based RL in APS cannot continuously learn. In this paper, a data-efficient RL method is constructed to learn from data by use of a model-based method. The proposed method uses a truncated Monte Carlo tree search to evaluate parking states and select moves. Two artificial neural networks are trained to provide the search probability of each tree branch and the final reward for each state using self-trained data. The data efficiency is enhanced by weighting exploration with parking trajectory r

SUBMITTER: Song S 

PROVIDER: S-EPMC7766926 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature