Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving
Fan Yang (Beijing Institute of Technology)
Xueyuan Li (Beijing Institute of Technology)
Qi Liu (Beijing Institute of Technology)
Z. Li (TU Delft - Transport and Planning, Beijing Institute of Technology)
Xin Gao (Beijing Institute of Technology)
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Abstract
In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the interactive information between agents in the environment into the decision-making process, this paper proposes a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL algorithm). The SGRL algorithm introduces graph convolution into the traditional deep neural network (DQN) algorithm, adopts the training method for a single agent, designs a more explicit incentive reward function, and significantly improves the dimension of the action space. The SGRL algorithm is compared with the traditional DQN algorithm (NGRL) and the multi-agent training algorithm (MGRL) in the highway ramp scenario. Results show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency.
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