强化学习算法与应用综述

Overview on Algorithms and Applications for Reinforcement Learning

强化学习是机器学习领域的研究热点,是考察智能体与环境的相互作用,做出序列决策、优化策略并最大化累积回报的过程.强化学习具有巨大的研究价值和应用潜力,是实现通用人工智能的关键步骤.本文综述了强化学习算法与应用的研究进展和发展动态,首先介绍强化学习的基本原理,包括马尔可夫决策过程、价值函数、探索-利用问题.其次,回顾强化学习经典算法,包括基于价值函数的强化学习算法、基于策略搜索的强化学习算法、结合价值函数和策略搜索的强化学习算法,以及综述强化学习前沿研究,主要介绍多智能体强化学习和元强化学习方向.最后综述强化学习在游戏对抗、机器人控制、城市交通和商业等领域的成功应用,以及总结与展望.

Reinforcement learning(RL)is a research hotpot in the machine learning area,which is considering a process of agent-environment interaction,sequential decision making,and total reward maximization.Reinforcement learning is worthy of in-depth research and a wide range of applications in the real world,and represents a vital step toward the Artificial General Intelligence(AGI).In this survey,we review the research progress and development in the algorithms and applications for reinforcement learning.We start with a brief review of the principle of reinforcement learning,including Markov decision process,value function,and exploration v.s.exploitation.Next,we discuss the traditional RL algorithms,including value-based algorithms,policy-based algorithms,and Actor-Critic algorithms,and further discuss the frontiers of RL algorithms,including multi-agent reinforcement learning and meta reinforcement learning.Then,we sketch some successful RL applications in the fields of games,robotics,urban traffic,and business.Finally,we summarize briefly and prospect the development trends of reinforcement learning.