Author: Zen and the Art of Computer Programming
The development of artificial intelligence (AI) has become an international consensus. In recent years, people have been open to AI technology, hoping to make AI technology more effective, comprehensive and systematically applied to all walks of life through new methods such as big data and cloud computing. In the field of education, AI has also been seen to improve the individualized ability of students, and it has also attracted the attention of some related researchers. With the continuous advancement and application of artificial intelligence technology, "Deep Reinforcement Learning" (Deep Reinforcement Learning, DRL) has gradually become a mainstream discipline. It can be used to solve the interaction problem between the agent and the environment, and optimize its own strategy based on this, so as to maximize the effect of school education. At present, many universities at home and abroad are also conducting DRL-based educational research, such as Tsinghua University's "Using Reinforcement Learning to Improve the Exploration Ability of Agents in the Environment" project; Peking University's "Smart Campus" project; Soochow University's "Smart Transportation" project. In short, the improvement and enhancement brought by DRL is gradually becoming a hot direction in education.
This article will combine personal experience to elaborate on the role of DRL in education and its significance in the development of artificial intelligence. The article first briefly introduces the research status and development trend of DRL in education, and then introduces the basic concepts, terms and basic models of DRL in detail. Next, we will explain how to use DRL to build an interactive system between an agent and the environment. Finally, some current relevant research results will be introduced, and the future development direction of DRL in education will be given.
2. Explanation of basic concepts and terms
2.1 DRL
DRL, or Deep Reinforcement Learning, is a subfield of machine learning. From the perspective of behaviorism, it formulates the decision-making problem as the interaction between the agent and the environment, and assumes that the actions of the agent will affect the state of the environment, and the environment provides rewards and punishments. It mainly consists of three parts: deep neural network, deep learning, and reinforcement learning.
A deep neural network refers to the use of multiple hidden