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In this paper, we focus on the basic form of autonomous follow driving problem with one leader and one follower. A reinforcement learning based throttle and brake control approach is developed for the follower vehicle. Near optimal control law is directly learned by “trial and error” with the neural dynamic programming algorithm. According to the timely updated following state, the learned control...
Due to the highly non-linear properties of the longitudinal dynamics of autonomous land vehicles (ALVs), it is difficult to tune the parameters of a speed controller for the autonomous driving of ALVs. Aiming at this problem, in this paper, a novel learning-Based speed controller is proposed, which is composed of a time-varying proportional-integral (PI) control structure and a learning-Based learning...
Approximate policy iteration (API) has been shown to be a class of reinforcement learning methods with stability and sample efficiency. However, sample collection is still an open problem which is critical to the performance of API methods. In this paper, a novel adaptive sample collection strategy using active learning-based exploration is proposed to enhance the performance of kernel-based API....
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