Accelerated Multi-objective Task Learning using Modified Q-learning Algorithm
<|reference_start|>Accelerated Multi-objective Task Learning using Modified Q-learning Algorithm: 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.<|reference_end|>
@article{rajamohan2024accelerated,
    title={Accelerated Multi-objective Task Learning using Modified Q-learning
  Algorithm},
    author={Varun Prakash Rajamohan, Senthil Kumar Jagatheesaperumal},
    journal={International Journal of Ad Hoc and Ubiquitous Computing Vol. 47,
  No. 1, Year: 2024},
    year={2024},
    doi={10.1504/IJAHUC.2024.140665},
    archivePrefix={arXiv},
    eprint={2409.01046},
    primaryClass={cs.RO cs.AI}
}