Policy-based Auto-Driving in Highway based on Distributional Reinforcement Learning Methods

This paper presents reinforcement learning-based learning methods for designing a supervisor for automatic driving in the highway environment. Due to the random driving conditions on the highway as well as the more realistic driving conditions, the benefits of deep distributed reinforcement learning have been exploited. In this paper, for the first time, the use of Fully Parameterized Quantile Function (FQF) and Implicit Quantile Network (IQN) distributed learning methods is proposed to learn driving policies. To train the agent using the camera data, the LIDAR sensor and its combination are suggested. In order to use the combination of the two types of data, we have employed a multi-input network structure. To evaluate the proposed methods, we have used the highway driving simulator developed in unity software. The realization of the car in the simulator is done with the help of driver assistance systems. Agent evaluation is based on a learning driving policy that can choose the right action to steer the car. In order to better evaluate the methods, we have examined the two criteria of speed changes and lane changes for learning driving policy. The results obtained from the article were compared with methods such as DQN, ‌QR-DQN that were previously presented. The results show that the proposed algorithms can learn appropriate driving policies in the highway environment. The FQF method also performs better than IQN and other strategies implemented in the past.