极限学习机（ELM）结合强化学习算法 —— 基于ELM的q-learning 算法 —— 基于elm的reinforcement learning

地址：

https://www.researchgate.net/publication/278711397_Reinforcement_Learning_Based_on_Extreme_Learning_Machine

Reinforcement Learning Based on Extreme Learning Machine

July 2012
DOI: 10.1007/978-3-642-31837-5_12

In book: Emerging Intelligent Computing Technology and Applications

Abstract and figures
Extreme learning machine not only has the best generalization performance but also has simple structure and convenient calculation. In this paper, its merits are used for reinforcement learning. The use of extreme learning machine on Q function approximation can improve the speed of reinforcement learning. As the number of hidden layer nodes is equal to that of samples, the larger sample size will seriously affect the learning speed. To solve this problem, a rolling time-window mechanism is introduced to the algorithm, which can reduce the size of the sample space to a certain extent. Finally, our algorithm is compared with a reinforcement learning based on a traditional BP neural network using a boat problem. Simulation results show that the proposed algorithm is faster and more effective.