Reinforcement learning model, algorithms and its application

Abstract

Reinforcement learning comes from the animal learning theory. RL does not need prior knowledge, it can autonomously get optional policy with the knowledge obtained by trial-and-error and continuously interacting with dynamic environment. Its characteristics of self improving and online learning make reinforcement learning become one of intelligent agent's core technologies. In this paper, we firstly survey the model and theory of reinforcement learning. Then, we roundly present the main reinforcement learning algorithms, including Sarsa, temporal difference, Q-learning and function approximation. Finally, we briefly introduce some applications of reinforcement learning and point out some future research directions of reinforcement learning.