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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...
Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This action-based or reinforcement learning can capture notions of optimal behavior occurring in natural systems. We describe mathematical formulations for reinforcement learning and a practical implementation method known as adaptive dynamic...
This paper describes the application of a novel reinforcement learning to the difficult real world problem of elevator dispatching. We propose a new algorithm combing Q-learning and residual gradient to solve this problem and obtain the results which are better than other traditional elevator control algorithms.
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