Smooth Q-learning: Accelerate Convergence of Q-learning Using Similarity
Artificial Intelligence
2021-06-03 v1
Abstract
An improvement of Q-learning is proposed in this paper. It is different from classic Q-learning in that the similarity between different states and actions is considered in the proposed method. During the training, a new updating mechanism is used, in which the Q value of the similar state-action pairs are updated synchronously. The proposed method can be used in combination with both tabular Q-learning function and deep Q-learning. And the results of numerical examples illustrate that compared to the classic Q-learning, the proposed method has a significantly better performance.
Cite
@article{arxiv.2106.01134,
title = {Smooth Q-learning: Accelerate Convergence of Q-learning Using Similarity},
author = {Wei Liao and Xiaohui Wei and Jizhou Lai},
journal= {arXiv preprint arXiv:2106.01134},
year = {2021}
}