mlsanityData to 14 Oct 2026

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

arXiv 2409.01046 ↗First posted 2 Sep 2024cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 23 references that are in the map. First posted on arXiv 2 Sep 2024.

Builds on

2 of 23 references.

References · 2

  1. 2023Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environmentcited by 13
  2. 2022Path Planning of Cleaning Robot with Reinforcement Learningcited by 7

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