# Mobile Networks for Computer Go

arXiv: https://arxiv.org/abs/2008.10080 · HTML: https://mlsanity.com/paper/2008.10080/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants](https://mlsanity.com/paper/2102.12375/) (2021)
- [Batch Monte Carlo Tree Search](https://mlsanity.com/paper/2104.04278/) (2021)
- [Improving Model and Search for Computer Go](https://mlsanity.com/paper/2102.03467/) (2021)
- [Spatial State-Action Features for General Games](https://mlsanity.com/paper/2201.06401/) (2022)
- [Vision Transformers for Computer Go](https://mlsanity.com/paper/2309.12675/) (2023)

## Abstract (arXiv)

The architecture of the neural networks used in Deep Reinforcement Learning programs such as Alpha Zero or Polygames has been shown to have a great impact on the performances of the resulting playing engines. For example the use of residual networks gave a 600 ELO increase in the strength of Alpha Go. This paper proposes to evaluate the interest of Mobile Network for the game of Go using supervised learning as well as the use of a policy head and a value head different from the Alpha Zero heads. The accuracy of the policy, the mean squared error of the value, the efficiency of the networks with the number of parameters, the playing speed and strength of the trained networks are evaluated.