Name
Abstract | ||
|---|---|---|
Among many reinforcement learning methods, FALCON is a machine learning method which is an extend fuzzy ART(Adaptive Resonance Theory), and can appropriately discretize a state space. FALCON is an on-line method proposed by Ah-Hwee Tan. It can discretize a state space and learn action rules simultaneously by learning relations among percepts, actions, and rewards. In this study, a learning agent using FALCON is interactively trained, and the learning effect is measured through experiments. In experiments, the learning agent learns by playing 50,000 card games of \"Hearts\" against three rule-based agents. Then, the interface that agents can interactively play the game with human cooperators is made so that human cooperators can play the game against the learning agent to strengthen it. It continues learning during games. The effectiveness of interactive learning is ascertained through the experiments. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.procs.2016.08.112 | KES |
Keywords | Field | DocType |
Reinforcement Learning,Multi-channel Adaprive Resonance Theory Networks,FALCON,Hearts | Robot learning,Interactive Learning,Adaptive resonance theory,Learning effect,Instance-based learning,Active learning (machine learning),Computer science,Artificial intelligence,Machine learning,Reinforcement learning,Learning classifier system | Conference |
Volume | Issue | ISSN |
96 | C | 1877-0509 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuma Kasahara | 1 | 0 | 0.34 |
| Kenta Nimoto | 2 | 2 | 1.07 |
| Kenichi Takahashi | 3 | 156 | 18.94 |
| Michimasa Inaba | 4 | 26 | 8.88 |