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.

Learning algorithms for Markov decision processes

1987 ◽  
Vol 24 (01) ◽  
pp. 270-276
Author(s):  
Masami Kurano
Keyword(s):  
Markov Decision Processes ◽  
Optimal Policy ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Decision Processes ◽  
The State ◽  
Reward Structure ◽  
Adaptive Policy ◽  
Markov Decision ◽  
Reward Criterion

This study is concerned with finite Markov decision processes whose dynamics and reward structure are unknown but the state is observable exactly. We establish a learning algorithm which yields an optimal policy and construct an adaptive policy which is optimal under the average expected reward criterion.

FMRQ—A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks

2017 ◽  
Vol 47 (6) ◽  
pp. 1367-1379 ◽  
Author(s):  
Zhen Zhang ◽  
Dongbin Zhao ◽  
Junwei Gao ◽  
Dongqing Wang ◽  
Yujie Dai
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Cooperative Tasks ◽  
Multiagent Reinforcement Learning ◽  
Reinforcement Learning Algorithm
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