Q-Learning with Differential Entropy of Q-Tables

TL;DR

This paper introduces Differential Entropy of Q-tables (DE-QT) as a novel external measure to detect information loss in Q-learning, helping determine optimal stopping points for training to improve efficiency and success rate.

Contribution

The paper proposes DE-QT as a new method to monitor information loss in Q-learning, providing a practical stopping criterion to enhance training effectiveness.

Findings

DE-QT effectively detects information loss during training.

Using DE-QT improves the balance between success rate and efficiency.

DE-QT identifies optimal stopping points in Q-learning training.

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.