Name
Title | ||
|---|---|---|
Accuracy-Based Learning Classifier Systems for Multistep Reinforcement Learning: A Fuzzy Logic Approach to Handling Continuous Inputs and Learning Continuous Actions. |
Abstract | ||
|---|---|---|
Despite their proven effectiveness, many Michigan learning classifier systems (LCSs) cannot perform multistep reinforcement learning in continuous spaces. To meet this technical challenge, some LCSs have been designed to learn fuzzy logic rules. They can be largely classified into strength-based and accuracy-based systems. The latter is gaining more research attention in the last decade. However, ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TEVC.2016.2560139 | IEEE Transactions on Evolutionary Computation |
Keywords | Field | DocType |
Learning (artificial intelligence),Learning systems,Algorithm design and analysis,Fuzzy logic,Ground penetrating radar,Geophysical measurement techniques,Benchmark testing | Robot learning,Online machine learning,Mathematical optimization,Semi-supervised learning,Stability (learning theory),Instance-based learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Machine learning,Learning classifier system | Journal |
Volume | Issue | ISSN |
20 | 6 | 1089-778X |
Citations | PageRank | References |
0 | 0.34 | 36 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Chen | 1 | 48 | 16.42 |
| Colin Douch | 2 | 4 | 1.20 |
| Mengjie Zhang | 3 | 3777 | 300.33 |