How an Adaptive Learning Rate Benefits Neuro-Fuzzy Reinforcement Learning Systems
| contributor author | Takashi Kuremoto | |
| contributor author | Masanao Obayashi | |
| contributor author | Kunikazu Kobayashi | |
| contributor author | Shingo Mabu | |
| date accessioned | 2020-03-12T14:47:18Z | |
| date available | 2020-03-12T14:47:18Z | |
| date issued | 2014 | |
| identifier other | z2WqHnhyY0G2YDYxFgwkUfIbrKeZlPCPggk_vhjp_xQWsX3OrR.pdf | |
| identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/902983?locale-attribute=en&show=full | |
| format | general | |
| language | English | |
| title | How an Adaptive Learning Rate Benefits Neuro-Fuzzy Reinforcement Learning Systems | |
| type | Journal Paper | |
| contenttype | Fulltext | |
| contenttype | Fulltext | |
| identifier padid | 7323424 | |
| identifier doi | 10.1007/978-3-319-11857-4_37 | |
| journal title | Lecture Notes in Computer Science | |
| coverage | Academic | |
| pages | 324-331 | |
| filesize | 337283 | |
| citations | 2 |