Name
Abstract | ||
|---|---|---|
In this paper, we propose Simple Reinforcement Learning for a reinforcement learning agent that has small memory. In the real world, learning is difficult because there are an infinite number of states and actions that need a large number of stored memories and learning times. To solve a problem, estimated values are categorized as ``GOOD" or ``NO GOOD" in the reinforcement learning process. Additionally, the alignment sequence of estimated values is changed because they are regarded as an important sequence themselves. We conducted some simulations and observed the influence of our methods. Several simulation results show no bad influence on learning speed. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICMLA.2011.127 | ICMLA), 2011 10th International Conference |
Keywords | Field | DocType |
learning (artificial intelligence),learning times,simple reinforcement learning,small memory agent,stored memories,Q-learning,Reinforcement learning,State-action set categorize | Temporal difference learning,Instance-based learning,Semi-supervised learning,Active learning (machine learning),Computer science,Q-learning,Unsupervised learning,Artificial intelligence,Machine learning,Learning classifier system,Reinforcement learning | Conference |
Volume | ISBN | Citations |
1 | 978-1-4577-2134-2 | 1 |
PageRank | References | Authors |
0.43 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akira Notsu | 1 | 1 | 0.43 |
| Katsuhiro Honda | 2 | 1 | 0.77 |
| Hidetomo Ichihashi | 3 | 370 | 72.85 |
| Yuki Komori | 4 | 5 | 1.45 |