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arXiv · 2409.01046

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

Varun Prakash Rajamohan·Senthil Kumar Jagatheesaperumal
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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.

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Varun Prakash Rajamohan, Senthil Kumar Jagatheesaperumal. 2024-09-02. Accelerated Multi-objective Task Learning using Modified Q-learning Algorithm. https://doi.org/10.1504/ijahuc.2024.140665

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