Reinforcement Learning Methods in Intelligent Systems
As one of the commonly used technologies in the field of artificial intelligence,reinforcement learning is listed alongside supervised learning and unsupervised learning as one of the three main machine learning paradigms.Reinforcement learning involves an agent interacting directly with the environment to learn the best strategy by maximizing cumulative rewards.This paper reviews the development of reinforcement learning,introduces classical algorithms and models in both reinforcement learning and deep reinforcement learning,including value function-based methods,policy gradient methods,actor-critic algorithms,deep Q-networks,and their optimization models.At the end of this paper,current research challenges and future prospects of reinforcement learning are discussed.
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