Reinforcement learning is an important branch of machine learning. It is a product of multidisciplinary and multi-domain intersection. Its essence is to solve the decision making problem, that is, to make decisions automatically and to make continuous decisions. It consists of four elements, agent, environmental status, actions, and rewards. The goal of intensive learning is to get the most cumulative rewards. This paper analyzes the definition of reinforcement learning through image, and expounds the difference between reinforcement learning and supervised learning. Finally, several practical applications of reinforcement learning are listed.

Qiu Jing. Analysis of Intensive Learning and Supervised Learning. Academic Journal of Computing & Information Science (2018) Vol. 1: 80-84.

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