首页|Reports from Mepco Schlenk Engineering College Provide New Insights into Robotic s (Accelerated Multi-objective Task Learning Using Modified q-learning Algorithm )
Reports from Mepco Schlenk Engineering College Provide New Insights into Robotic s (Accelerated Multi-objective Task Learning Using Modified q-learning Algorithm )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsCurrent study results on Robotics have been publi shed. According to news reporting from Tamil Nadu, India, by NewsRx journalists, research stated, "Robots find extensive applications in industry. In recent yea rs, the influence of robots has also increased rapidly in domestic scenarios." The news correspondents obtained a quote from the research from Mepco Schlenk En gineering College, "The Q-learning algorithm aims to maximise the reward for rea ching the goal. This paper proposes a modified version of the Q-learning algorit hm, known as Q-learning with scaled distance metric (Q -SD). This algorithm enh ances task learning and makes task completion more meaningful. A robotic manipul ator (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 in to grids of different dimensions. The first has a grid count of 3 x 3, and the s econd 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."
Tamil NaduIndiaAsiaAlgorithmsEme rging TechnologiesMachine LearningNano-robotRoboticsMepco Schlenk Engine ering College
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