A pulse neural network reinforcement learning algorithm for partially observable Markov decision processes

2005 ◽  
Vol 36 (3) ◽  
pp. 42-52 ◽  
Author(s):  
Koichiro Takita ◽  
Masafumi Hagiwara
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Markov Decision Processes ◽  
Learning Algorithm ◽  
Decision Processes ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
Partially Observable ◽  
Reinforcement Learning Algorithm

scholarly journals 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

2021 ◽  
Vol 3 (3) ◽  
pp. 554-581
Author(s):  
Xuanchen Xiang ◽  
Simon Foo
Keyword(s):  
Natural Language Processing ◽  
Reinforcement Learning ◽  
Natural Language ◽  
Markov Decision Processes ◽  
Language Processing ◽  
Decision Processes ◽  
Recent Advances ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
Partially Observable

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.

A New Reinforcement Learning Algorithm With Fixed Exploration for Semi-Markov Control in Preventive Maintenance

2017 ◽  
Author(s):  
Angelo Encapera ◽  
Abhijit Gosavi
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Processes ◽  
Numerical Experiments ◽  
Learning Algorithm ◽  
Decision Processes ◽  
Control Problems ◽  
Total Productive Maintenance ◽  
Markov Decision ◽  
Markov Control ◽  
Reinforcement Learning Algorithm

Artificial intelligence techniques can play a significant role in solving problems encountered in the domain of Total Productive Maintenance (TPM). This paper considers a new reinforcement learning algorithm called iSMART, which can solve semi-Markov decision processes underlying control problems related to TPM. The algorithm uses a constant exploration rate, unlike its precursor R-SMART, which required exploration decay. Numerical experiments conducted here show encouraging behavior with the new algorithm.

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