Accelerated Multi-objective Task Learning using Modified Q-learning Algorithm

TL;DR

This paper introduces a modified Q-learning algorithm called Q-SD that improves task learning efficiency and effectiveness in robotic table cleaning by minimizing movement distance and increasing success rates.

Contribution

It proposes the Q-SD algorithm, a novel modification to Q-learning that enhances task learning and movement efficiency in robotic applications.

Findings

Success rate of 86% and 59% in two grid environments.

Reduction in movement distance by 8.61% and 6.7%.

Improved task completion efficiency over traditional Q-learning.

Abstract

Robots find extensive applications in industry. In recent years, the influence of robots has also increased rapidly in domestic scenarios. The Q-learning algorithm aims to maximise the reward for reaching the goal. This paper proposes a modified version of the Q-learning algorithm, known as Q-learning with scaled distance metric (Q-SD). This algorithm enhances task learning and makes task completion more meaningful. A robotic manipulator (agent) applies the Q-SD algorithm to the task of table cleaning. Using Q-SD, the agent acquires the sequence of steps necessary to accomplish the task while minimising the manipulator's movement distance. We partition the table into grids of different dimensions. The first has a grid count of 3 times 3, and the second has a grid count of 4 times 4. Using the Q-SD algorithm, the maximum success obtained in these two environments was 86% and 59% respectively. Moreover, Compared to the conventional Q-learning algorithm, the drop in average distance moved by the agent in these two environments using the Q-SD algorithm was 8.61% and 6.7% respectively.