Research on Intelligent Decision-Making of Robots Based on Neural Network and Experience Playback

This study delves into the advancement of intelligent decision-making in robotics through the integration of deep learning technologies. With robotics becoming increasingly vital in industry, healthcare, and household settings, their adaptability and effectiveness in dynamic environments remain a challenge. Traditional rule-based decision systems are limited by their rigidity and inability to cope with unforeseen scenarios. The advent of deep learning, offering promising avenues in image recognition, speech recognition, and natural language processing, proposes a novel approach to robotics decision-making. By employing deep learning models, robots can autonomously learn and optimize their actions, enhancing their performance in complex settings. This research explores the application of deep reinforcement learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) in improving robotic perception, decision-making, and actions. Our findings aim to extend the applicability of deep learning in robotics, contributing to the design and implementation of future intelligent robotic systems.