Generalized Rapid Action Value Estimation

Cazenave, Tristan

Generalized Rapid Action Value Estimation

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 754-759.

Abstract

Monte Carlo Tree Search (MCTS) is the state of the art algorithm for many games including the game of Go and General Game Playing (GGP). The standard algorithm for MCTS is Upper Confidence bounds applied to Trees (UCT). For games such as Go a big improvement over UCT is the Rapid Action Value Estimation (RAVE) heuristic. We propose to generalize the RAVE heuristic so as to have more accurate estimates near the leaves. We test the resulting algorithm named GRAVE for Atarigo, Knighthrough, Domineering and Go.

Tristan Cazenave. 2015. Generalized Rapid Action Value Estimation. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 754–759. https://ijcai.org/proceedings/2015/112

@inproceedings{ijcai2015p112,
  title     = {Generalized Rapid Action Value Estimation},
  author    = {Cazenave, Tristan},
  booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-15}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Qiang Yang and Michael Wooldridge},
  pages     = {754--759},
  year      = {2015},
  month     = {7},
  note      = {Main Track},
  url       = {https://ijcai.org/proceedings/2015/112},
}

TY  - CPAPER
TI  - Generalized Rapid Action Value Estimation
AU  - Cazenave, Tristan
T2  - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2015
SP  - 754
EP  - 759
UR  - https://ijcai.org/proceedings/2015/112
ER  -