Name
Abstract | ||
|---|---|---|
In order to carry out simulation using an environment in the real world, reinforcement learning techniques are improved to be applied on continuous environment. Real values are available as the input and output value of neural networks, however, it can not be applied to the multi step problem. Although the classifier system can preserve the value of actions and its prediction accuracy, there exists a problem when applying on continuous environment, because the bit string in the condition part should be long. In order to compensate for the disadvantages of both the neural network and the classifier system, this paper proposes N-XCS (Neural network eXtended Classifier System) in which the merits of these two methods are twisted together. Additionally, the usefulness of the proposed method is indicated based on the result of some numerical experiments. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMC.2018.00042 | 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
Keywords | Field | DocType |
continuous environment,classifier system,neural network | Existential quantification,Computer science,Input/output,Artificial intelligence,Classifier (linguistics),Artificial neural network,Bit array,Machine learning,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-5386-6651-7 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomohiro Hayashida | 1 | 29 | 11.56 |
| Ichiro Nishizaki | 2 | 443 | 42.37 |
| Shinya Sekizaki | 3 | 0 | 2.37 |
| Yuki Ogasawara | 4 | 0 | 0.34 |