Name
Abstract | ||
|---|---|---|
Reinforcement Learning (RL) is an intuitive way of programming well-suited for use on autonomous robots because it does not need to specify how the task has to be achieved. However, RL remains difficult to implement in realistic domains because of its slowness in convergence. In this paper, we develop a theoretical study of the influence of some RL parameters over the learning speed. We also provide experimental justifications for choosing the reward function and initial Q-values in order to improve RL speed within the context of a goal-directed robot task. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IROS.2006.282341 | 2006 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-12 |
Keywords | Field | DocType |
learning artificial intelligence,indexing terms,reinforcement learning,robot control | Robot learning,Convergence (routing),Robot control,Computer science,Artificial intelligence,Robot,Slowness,Reinforcement learning,Robot programming | Conference |
Citations | PageRank | References |
3 | 0.43 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laëtitia Matignon | 1 | 88 | 9.43 |
| Guillaume J. Laurent | 2 | 97 | 12.60 |
| Nadine Le Fort-Piat | 3 | 77 | 10.09 |