Besides, there are three innovations in the proposed learning network: raw screen outputs are the only information which the driving agent can rely on, a weighted layer that enhances the differences of the lengthy episode, and a modified replay mechanism that overcomes the problem of sparsity and accelerates learning. The main contribution of this study is that we trained the driving agent using a brain-inspired trial-and-error technique, which was in line with the real world situation. In this study, we propose an end-to-end architecture using novel deep-Q-network architecture in conjunction with a recurrence to resolve the problem in the field of simulated self-driving. Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of making a self-driving agent control in a human-like way. From the perspective of brain inspiration, reinforcement learning has gained additional interest in solving decision-making tasks as increasing neuroscientific research demonstrates that significant links exist between reinforcement learning and specific neural substrates. In recent years, bio-inspired learning methods have emerged, and they are different from the classical mathematical programming principle. 3Department of Computer Science, Technical University of Munich, Munich, GermanyĪn effective way to achieve intelligence is to simulate various intelligent behaviors in the human brain.2School of Computer Science, Fudan University, Shanghai, China.1Department of Computer Science, College of Electronics and Information Engineering, Tongji University, Shanghai, China.Jieneng Chen 1 *, Jingye Chen 2, Ruiming Zhang 1 and Xiaobin Hu 3 *