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 x 3, and the second has a grid count of 4 x 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.