Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems: Part 1—Fundamentals and Applications in Games, Robotics and Natural Language Processing

• Published in: Machine learning and knowledge extraction Vol. 3; no. 3; pp. 554 - 581
• Main Authors: Xiang, Xuanchen, Foo, Simon
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2021
• Subjects: Algorithms; Deep learning; deep reinforcement learning; Dynamic programming; Expected utility; Games; Machine learning; Markov analysis; Markov decision process; Markov processes; Natural language processing; partially observable Markov decision process; Probability; Probability distribution; reinforcement learning; Robotics; Transportation applications
• ISSN: 2504-4990; 2504-4990
• DOI: 10.3390/make3030029

The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems. Reinforcement Learning (RL) is an approach to simulate the human’s natural learning process, whose key is to let the agent learn by interacting with the stochastic environment. The fact that the agent has limited access to the information of the environment enables AI to be applied efficiently in most fields that require self-learning. Although efficient algorithms are being widely used, it seems essential to have an organized investigation—we can make good comparisons and choose the best structures or algorithms when applying DRL in various applications. In this overview, we introduce Markov Decision Processes (MDP) problems and Reinforcement Learning and applications of DRL for solving POMDP problems in games, robotics, and natural language processing. A follow-up paper will cover applications in transportation, communications and networking, and industries.