Name
Abstract | ||
|---|---|---|
Using deep convolutional neural networks for move prediction has led to massive progress in computer Go. Like Go, Hex has a large branching factor that limits the success of shallow and selective search. We show that deep convolutional neural networks can be used to produce reliable move evaluation in the game of Hex. We begin by collecting self-play games of MoHex 2.0. We then train the neural ne... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TG.2017.2785042 | IEEE Transactions on Games |
Keywords | DocType | Volume |
Bridges,Games,Resistance,Monte Carlo methods,Computational modeling | Journal | 10 |
Issue | ISSN | Citations |
4 | 2475-1502 | 1 |
PageRank | References | Authors |
0.39 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Gao | 1 | 42 | 5.78 |
| Ryan Hayward | 2 | 2 | 1.09 |
| Martin Müller | 3 | 8 | 1.61 |