# Improving Model and Search for Computer Go

arXiv: https://arxiv.org/abs/2102.03467 · HTML: https://mlsanity.com/paper/2102.03467/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 6 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Thinking Fast and Slow with Deep Learning and Tree Search](https://mlsanity.com/paper/1705.08439/) (2017)
- [Accelerating Self-Play Learning in Go](https://mlsanity.com/paper/1902.10565/) (2019)
- [ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero](https://mlsanity.com/paper/1902.04522/) (2019)
- [Polygames: Improved Zero Learning](https://mlsanity.com/paper/2001.09832/) (2020)
- [Mobile Networks for Computer Go](https://mlsanity.com/paper/2008.10080/) (2020)

## Built on by (most cited first, 3 of 3)

- [DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning](https://mlsanity.com/paper/2204.02558/) (2022)
- [Batch Monte Carlo Tree Search](https://mlsanity.com/paper/2104.04278/) (2021)
- [Vision Transformers for Computer Go](https://mlsanity.com/paper/2309.12675/) (2023)

## Abstract (arXiv)

The standard for Deep Reinforcement Learning in games, following Alpha Zero, is to use residual networks and to increase the depth of the network to get better results. We propose to improve mobile networks as an alternative to residual networks and experimentally show the playing strength of the networks according to both their width and their depth. We also propose a generalization of the PUCT search algorithm that improves on PUCT.