  An effective way to achieve intelligence is to simulate various intelligent
behaviors in the human brain. In recent years, bio-inspired learning methods
have emerged, and they are different from the classical mathematical
programming principle. In 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. 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 a self-driving agent control in a human-like way. 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. 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. 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 proposed
network was trained and tested under a third-partied OpenAI Gym environment.
After training for several episodes, the resulting driving agent performed
advanced behaviors in the given scene. We hope that in the future, the proposed
brain-inspired learning system would inspire practicable self-driving control
solutions.
