Research on AI Empowered Teaching Agents Based on Q-learning+DDPG

With the rapid development of artificial intelligence technology, its application in the field of education is becoming increasingly widespread. As a mainstream form of artificial intelligence, intelligent agents have brought revolutionary changes to teaching with their autonomy, adaptability, and interactive abilities. AI empowered teaching agents refer to the use of artificial intelligence technology, especially the reinforcement learning algorithm (Q-learning), to construct intelligent agents that can autonomously learn, make decisions, and adapt to the teaching environment. Ability to analyze students' learning data, understand their learning needs, and provide personalized learning advice and resources for students. This article explores the application of intelligent agents in teaching, analyzes their advantages and limitations, and combines Q-learning with the Deep Deterministic Policy Gradient (DDPG) algorithm to make up for the shortcomings of Q-learning by utilizing the advantages of DDPG algorithm in processing continuous action spaces. In addition, it is also possible to consider combining Q-learning with algorithms such as Monte Carlo Tree Search (MCTS) to improve the decision-making ability and learning efficiency of the agent.