Name
Abstract | ||
|---|---|---|
In recent years there has been much interest in the Monte Carlo tree search algorithm, a new, adaptive, randomized optimization algorithm. In fields as diverse as Artificial Intelligence, Operations Research, and High Energy Physics, research has established that Monte Carlo tree search can find good solutions without domain dependent heuristics. However, practice shows that reaching high performance on large parallel machines is not so successful as expected. This paper proposes a new method for parallel Monte Carlo tree search based on the pipeline computation pattern. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Distributed, Parallel, and Cluster Computing | Mathematical optimization,Monte Carlo method,Monte Carlo tree search,Search algorithm,Markov chain Monte Carlo,Computer science,Depth-first search,Hybrid Monte Carlo,Algorithm,Quasi-Monte Carlo method,Monte Carlo integration,Distributed computing |
DocType | Volume | Citations |
Journal | abs/1605.04447 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| s ali mirsoleimani | 1 | 2 | 2.74 |
| Aske Plaat | 2 | 524 | 72.18 |
| H. Jaap van den Herik | 3 | 861 | 137.51 |
| Jos Vermaseren | 4 | 5 | 4.15 |