Name
Title | ||
|---|---|---|
Evaluation of a family of reinforcement learning cross-domain optimization heuristics |
Abstract | ||
|---|---|---|
In our participation to the Cross-Domain Heuristic Search Challenge (CHeSC 2011) [1] we developed an approach based on Reinforcement Learning for the automatic, on-line selection of low-level heuristics across different problem domains. We tested different memory models and learning techniques to improve the results of the algorithm. In this paper we report our design choices and a comparison of the different algorithms we developed. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34413-8_32 | LION |
Keywords | Field | DocType |
different algorithm,low-level heuristics,cross-domain heuristic search challenge,different problem domain,different memory model,design choice,cross-domain optimization heuristics,reinforcement learning,on-line selection | Mathematical optimization,Heuristic,Computer science,Function learning,Heuristics,Artificial intelligence,Machine learning,Reinforcement learning,Learning classifier system | Conference |
Citations | PageRank | References |
12 | 0.60 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Di Gaspero | 1 | 679 | 43.61 |
| Tommaso Urli | 2 | 79 | 8.66 |