Abstract:

As an important method to solve sequential decision problems, reinforcement learning adopts a mechanism of “trial and error” to interact with the environment, in order to learn the policy of the task. Know-ledge, as a kind of structured information, which contains the elements of experience, values, cognitive rules and expert opinions, can be effectively used to improve the learning efficiency of reinforcement learning. This paper takes the basic theory of reinforcement learning as a starting point, and systematically summarizes the deep reinforcement learning and knowledge-based reinforcement learning.