Name
Title | ||
|---|---|---|
Information compression effect based on PCA for reinforcement learning agents' communication |
Abstract | ||
|---|---|---|
In general, the amount of required memory and reduction of learning time with explosion of the number of states become problems in reinforcement learning. In this study, as a method of cutting information of the learning table, principal component analysis was used as an information compression method, which is well known. The principal component analysis was applied to the learning table directly. Also, the influence given by reducing the principal component vector extremely when restructuring state space and action space was reviewed. In a numerical experiment, it was confirmed that the proposed method cut the amount of information and without a big change to learning speed and agents' minimal communication can had positive effect on the learning. |
Year | Venue | Keywords |
|---|---|---|
2012 | Joint International Conference on Soft Computing and Intelligent Systems SCIS and International Symposium on Advanced Intelligent Systems ISIS | Reinforcement Learning,Information Compression,Principal Component Analysis |
Field | DocType | ISSN |
Online machine learning,Competitive learning,Stability (learning theory),Semi-supervised learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Machine learning,Learning classifier system,Reinforcement learning | Conference | 2377-6870 |
Citations | PageRank | References |
2 | 0.38 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akira Notsu | 1 | 146 | 42.93 |
| Katsuhiro Honda | 2 | 289 | 63.11 |
| Hidetomo Ichihashi | 3 | 370 | 72.85 |
| ayaka ido | 4 | 2 | 0.38 |
| Yuki Komori | 5 | 5 | 1.45 |