Name
Title | ||
|---|---|---|
Concurrent modular Q-learning with local rewards on linked multi-component robotic systems |
Abstract | ||
|---|---|---|
Applying conventional Q-Learning to Multi-Component Robotic Systems (MCRS) increasing the number of components produces an exponential growth of state storage requirements. Modular approaches limit the state size growth to be polynomial on the number of components, allowing more manageable state representation and manipulation. In this article, we advance on previous works on a modular Q-learning approach to learn the distributed control of a Linked MCRS. We have chosen a paradigmatic application of this kind of systems using only local rewards: a set of robots carrying a hose from some initial configuration to a desired goal. The hose dynamics are simplified to be a distance constraint on the robots positions. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21344-1_16 | IWINAC (1) |
Keywords | Field | DocType |
hose dynamic,state storage requirement,linked mcrs,multi-component robotic system,concurrent modular q-learning,manageable state representation,local reward,exponential growth,multi-component robotic systems,state size growth,robots position,modular q-learning approach,modular approach | Robotic systems,State representation,Polynomial,Computer science,Q-learning,Artificial intelligence,Modular design,Robot,Machine learning,Exponential growth | Conference |
Volume | ISSN | Citations |
6686 | 0302-9743 | 1 |
PageRank | References | Authors |
0.38 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Borja Fernandez-Gauna | 1 | 49 | 4.89 |
| Jose Manuel Lopez-Guede | 2 | 22 | 3.25 |
| Manuel Graña | 3 | 1367 | 156.11 |