# Learning to Play Othello with Deep Neural Networks

arXiv: https://arxiv.org/abs/1711.06583 · HTML: https://mlsanity.com/paper/1711.06583/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2017.

## Built on by (most cited first, 6 of 6)

- [Revisiting the Othello World Model Hypothesis](https://mlsanity.com/paper/2503.04421/) (2025)
- [AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time](https://mlsanity.com/paper/2204.13307/) (2022)
- [OLIVAW: Mastering Othello without Human Knowledge, nor a Fortune](https://mlsanity.com/paper/2103.17228/) (2021)
- [Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples](https://mlsanity.com/paper/2106.14642/) (2021)
- [Gapoera: Application Programming Interface for AI Environment of Indonesian Board Game](https://mlsanity.com/paper/2110.11924/) (2021)
- [Word Play for Playing Othello (Reverses)](https://mlsanity.com/paper/2207.08766/) (2022)

## Abstract (arXiv)

Achieving superhuman playing level by AlphaGo corroborated the capabilities of convolutional neural architectures (CNNs) for capturing complex spatial patterns. This result was to a great extent due to several analogies between Go board states and 2D images CNNs have been designed for, in particular translational invariance and a relatively large board. In this paper, we verify whether CNN-based move predictors prove effective for Othello, a game with significantly different characteristics, including a much smaller board size and complete lack of translational invariance. We compare several CNN architectures and board encodings, augment them with state-of-the-art extensions, train on an extensive database of experts' moves, and examine them with respect to move prediction accuracy and playing strength. The empirical evaluation confirms high capabilities of neural move predictors and suggests a strong correlation between prediction accuracy and playing strength. The best CNNs not only surpass all other 1-ply Othello players proposed to date but defeat (2-ply) Edax, the best open-source Othello player.