An Adaptive Agent Decision Model Based on Deep Reinforcement Learning and Autonomous Learning

. This research focuses on the research of adaptive agent decision model based on deep reinforcement learning and autonomous learning. With the rapid development of artificial intelligence, the role of agents in decision-making tasks is becoming more and more important. However, traditional decision models tend to perform poorly in the face of complex and uncertain environments. Therefore, this study aims to propose a new decision-making model that enables agents to make adaptive decisions in complex environments by combining the techniques of deep reinforcement learning and autonomous learning. The significance and purpose of this study is to promote the development of the agent decision model and provide a new way to solve complex decision problems. Traditional decision models often face challenges in complex environments, but the adaptive agent decision model based on deep reinforcement learning and autonomous learning has better adaptability and generalization ability.