Reinforcement Learning from Algorithm Model to Industry Innovation: A Foundation Stone of Future Artificial Intelligence

doi:10.12142/ZTECOM.201903006

ZTE Communications ›› 2019, Vol. 17 ›› Issue (3): 31-41.DOI: 10.12142/ZTECOM.201903006

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Reinforcement Learning from Algorithm Model to Industry Innovation: A Foundation Stone of Future Artificial Intelligence

DONG Shaokang, CHEN Jiarui, LIU Yong, BAO Tianyi, GAO Yang

State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210008, China

Received:2019-07-10 Online:2019-09-29 Published:2019-12-06
About author:DONG Shaokang (shaokangdong@gmail.com) obtained his B.S. degree from the Advanced Class of Huazhong University of Science and Technology, China in 2018. He is currently a Ph.D. student in the Department of Computer Science and Technology, Nanjing University, China. His research interests include machine learning, reinforcement learning, and multi-armed bandits.|CHEN Jiarui obtained his B.S. degree from Dongbei University of Finance and Economics, China in 2018. He is currently a master student in the Department of Computer Science and Technology, Nanjing University, China. His research interests include machine learning, multi-agent reinforcement learning, and game.|LIU Yong received a B.S degree in communication engineering from China Agricultural University, China in 2017. He is currently a master student in the Department of Computer Science and Technology, Nanjing University, China. His current research interests include reinforcement learning, multi-agent learning, and transfer learning.|BAO Tianyi is an undergraduate student currently studying in the University of Michigan, USA. She studies computer science and psychology and will receive her B.S. degree in 2020. Her current research interests include the machine learning and human-computer interaction.|GAO Yang received the Ph.D. degree in computer software and theory from the Department of Computer Science and Technology, Nanjing University, China in 2000. He is a professor with the Department of Computer Science and Technology, Nanjing University. His current research interests include artificial intelligence and machine learning. He has published over 100 papers in top international conferences and journals.

Abstract

Abstract:

Reinforcement learning (RL) algorithm has been introduced for several decades, which becomes a paradigm in sequential decision-making and control. The development of reinforcement learning, especially in recent years, has enabled this algorithm to be applied in many industry fields, such as robotics, medical intelligence, and games. This paper first introduces the history and background of reinforcement learning, and then illustrates the industrial application and open source platforms. After that, the successful applications from AlphaGo to AlphaZero and future reinforcement learning technique are focused on. Finally, the artificial intelligence for complex interaction (e.g., stochastic environment, multiple players, selfish behavior, and distributes optimization) is considered and this paper concludes with the highlight and outlook of future general artificial intelligence.

Key words: artificial intelligence, decision-making and control problems, reinforcement learning

DONG Shaokang, CHEN Jiarui, LIU Yong, BAO Tianyi, GAO Yang. Reinforcement Learning from Algorithm Model to Industry Innovation: A Foundation Stone of Future Artificial Intelligence[J]. ZTE Communications, 2019, 17(3): 31-41.

Figures/Tables 9

Figure 1. The development trajectory of reinforcement learning technique.

Figure 2. The interaction between the agent and environment.

Figure 3. The classification of different reinforcement learning methods.

Table 1 Comparisons between reinforcement learning (RL) methods

Category	DP	On-policy MC	Off-policy MC	SARSA	Q-learning
Model-based	√
Model-free		√	√	√	√
On-policy		√		√
Off-policy			√		√
Bootstrapping	√			√	√

Figure 4. Neural network of the deep Q-learning [12].

Figure 5. The Gym games (from left to right and top to bottom: CarRacing, Mountainair, Ant, RoboschoolHumanoidFlagrunHarder, FetchPickAndPlace and MontezumaRevenge).

Figure 6. The different networks of AlphaGo [19].

Figure 7. The process of MCTS [19].

Figure 8. The network of AlphaZero [28].

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Reinforcement Learning from Algorithm Model to Industry Innovation: A Foundation Stone of Future Artificial Intelligence

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