Research on the Application of Reinforcement Learning Algorithms in Intelligent Robot Learning and Knowledge Fusion
Keywords:
Intelligent Robots, Time-Wrapping, Reinforcement Learning, Task Assignment, ClassificationAbstract
Intelligent robotics holds the promise of revolutionizing various industries by enhancing automation, efficiency, and adaptability. However, the integration of heterogeneous data from multiple sensors in dynamic environments poses significant challenges for efficient robot learning and decision-making. This paper proposed a novel approach, Dynamic Time Warping Reinforcement Learning (DTWRL) to perform data fusion challenges in intelligent robot learning. The proposed DTWRL model uses multiple data from the sensor environment for the collection of information in the robots. The model uses dynamic time warping with the computation of the time for the data transmission between the intelligent robots. The DTWRL model combines reinforcement learning with dynamic time warping, enabling the fusion of data collected at varying time intervals and handling variations in robot speed. With application of the dynamic time warping, the model efficiently measures the similarity between experiences, allowing robots to learn from each other's experiences and generalize across diverse environments. Simulation results demonstrated that the effectiveness of the DTWRL model in accurately classifying tasks and achieving high cumulative rewards. Comparative analysis with traditional machine learning models like SVM and Decision Tree shows that the DTWRL model outperforms in terms of accuracy, precision, recall, and F1 score.
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