# Design of an Intelligent Vehicle Behavior Decision Algorithm Based on DGAIL
###### Abstract
Imitation learning can learn existing expert knowledge, convert the expert knowledge into learning samples, and use the expert knowledge to guide the training of the agent, which reduces the number of ineffective exploration problems encountered by the agent in the training process and reduces the training time cost and computing cost, effectively improving the learning performance of deep reinforcement learning [9]. There are three commonly used imitation learning methods, namely, behavior cloning, inverse reinforcement learning, and generative adversarial imitation learning. Generative adversarial imitation learning (GAIL) is an imitation learning framework based on a generative adversarial network (GAN), which consists of a generator and a discriminator. The discriminator provides a reward value to the generator by identifying the difference between the expert strategy and the learned strategy. Similarly to the generator training method in the GAN, after obtaining the reward value given by  
Figure 1: Intelligent driving system block diagram. the discriminator, GAIL uses the TRPO and PPO algorithms to carry out strategy training. Generative adversarial imitation learning learns policies from a small number of expert trajectories and uses the discriminator to generate a reward value function, which reduces the demand for training samples and the computational complexity. In 2016, [PERSON] [10] proposed generative adversarial imitation learning (GAIL) based on a GAN network. In 2017, [PERSON] [11] proposed a multimodal biomimetic learning framework, using a GAN network to establish a multimodal biomimetic learning framework from unstructured and unlabeled teaching samples. In 2017, [PERSON] [12] proposed a hierarchical strategy framework based on reinforcement learning, using generative adversarial imitation learning to train low-level controllers in the acquired action dataset and reinforcement learning to train high-level controllers. In 2017, [PERSON] [13] proposed infogenerative adversarial imitation learning (Info-Gail), which combined the Info-Gan network with the Gail algorithm so that the agent can better understand the meaning of expert data. In 2018, [PERSON] [14] proposed multigenerative adversarial imitation learning (Multi-Gail), which combined a multiagent system and the Gail algorithm to extend the Gail algorithm to the multiagent field. In summary, the depth of the reinforcement learning for the intelligent vehicle behavior decision model has good adaptability and generalization. This paper is based on related research projects. An intelligent vehicle is the research object, and the depth is proposed based on the reinforcement learning method to design an intelligent vehicle frame of decision-making behavior. To conduct thorough research on the decision-making algorithm, the proposed DGAIL was used to study the intelligent decision-making system. The DQN algorithm is proposed to serve as the generation network of GAIL to realize the transformation of the original GAIL algorithm and to make it more suitable for an intelligent vehicle decision-making system. The research in this paper combines theoretical analysis and simulation experiments. The test platform was built based on the Pygame module. Considering the function of the decision-making system, the scenarios of straight road and merging road in the structured road were selected as the simulation scenarios. By setting road and vehicle parameters, lane keeping, lane changing, on-ramp incorporation, entry and exit roundabouts, and other functions were verified. The behavior decision model based on the DGAIL algorithm was designed to realize autonomous decision-making by intelligent vehicles on structured roads, and the effectiveness and stability of the algorithm in straight and merge scenarios were verified in the simulation environment.