Accelerated Multi-objective Task Learning using Modified Q-learning Algorithm
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arXiv
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| Format: | Preprint |
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2024
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| author | Rajamohan, Varun Prakash Jagatheesaperumal, Senthil Kumar |
| author_facet | Rajamohan, Varun Prakash Jagatheesaperumal, Senthil Kumar |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_01046 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerated Multi-objective Task Learning using Modified Q-learning Algorithm Rajamohan, Varun Prakash Jagatheesaperumal, Senthil Kumar Robotics Artificial Intelligence 68T05, 93C85, 93B40, 90C29 I.2.6; I.2.9; I.2.8; F.1.1; F.2.1; H.1.2; G.1.6 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. |
| title | Accelerated Multi-objective Task Learning using Modified Q-learning Algorithm |
| topic | Robotics Artificial Intelligence 68T05, 93C85, 93B40, 90C29 I.2.6; I.2.9; I.2.8; F.1.1; F.2.1; H.1.2; G.1.6 |
| url | https://arxiv.org/abs/2409.01046 |