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arXiv · 2006.14795

Q-Learning with Differential Entropy of Q-Tables

Tung D. Nguyen·Kathryn E. Kasmarik·Hussein A. Abbass
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Abstract

It is well-known that information loss can occur in the classic and simple Q-learning algorithm. Entropy-based policy search methods were introduced to replace Q-learning and to design algorithms that are more robust against information loss. We conjecture that the reduction in performance during prolonged training sessions of Q-learning is caused by a loss of information, which is non-transparent when only examining the cumulative reward without changing the Q-learning algorithm itself. We introduce Differential Entropy of Q-tables (DE-QT) as an external information loss detector to the Q-learning algorithm. The behaviour of DE-QT over training episodes is analyzed to find an appropriate stopping criterion during training. The results reveal that DE-QT can detect the most appropriate stopping point, where a balance between a high success rate and a high efficiency is met for classic Q-Learning algorithm.

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Tung D. Nguyen, Kathryn E. Kasmarik, Hussein A. Abbass. 2020-06-26. Q-Learning with Differential Entropy of Q-Tables. https://arxiv.org/abs/2006.14795

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