# Giraffe: Using Deep Reinforcement Learning to Play Chess

arXiv: https://arxiv.org/abs/1509.01549 · HTML: https://mlsanity.com/paper/1509.01549/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2015.

## Built on by (most cited first, 12 of 12)

- [Amortized Planning with Large-Scale Transformers: A Case Study on Chess](https://mlsanity.com/paper/2402.04494/) (2024)
- [PUZZLES: A Benchmark for Neural Algorithmic Reasoning](https://mlsanity.com/paper/2407.00401/) (2024)
- [Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding](https://mlsanity.com/paper/2308.05893/) (2023)
- [DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization](https://mlsanity.com/paper/2502.07237/) (2025)
- [Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks](https://mlsanity.com/paper/2206.01812/) (2022)
- [Learning to Plan via Supervised Contrastive Learning and Strategic Interpolation: A Chess Case Study](https://mlsanity.com/paper/2506.04892/) (2025)
- [Learning to Identify Top Elo Ratings: A Dueling Bandits Approach](https://mlsanity.com/paper/2201.04480/) (2022)
- [Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2502.04402/) (2025)
- [Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs](https://mlsanity.com/paper/2505.06319/) (2025)
- [Gapoera: Application Programming Interface for AI Environment of Indonesian Board Game](https://mlsanity.com/paper/2110.11924/) (2021)
- [Data-Driven Evaluation of Training Action Space for Reinforcement Learning](https://mlsanity.com/paper/2204.03840/) (2022)
- [Learning to Play: Reinforcement Learning and Games](https://mlsanity.com/paper/2610.17391/) (2026)

## Abstract (arXiv)

This report presents Giraffe, a chess engine that uses self-play to discover all its domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer. Unlike previous attempts using machine learning only to perform parameter-tuning on hand-crafted evaluation functions, Giraffe's learning system also performs automatic feature extraction and pattern recognition. The trained evaluation function performs comparably to the evaluation functions of state-of-the-art chess engines - all of which containing thousands of lines of carefully hand-crafted pattern recognizers, tuned over many years by both computer chess experts and human chess masters. Giraffe is the most successful attempt thus far at using end-to-end machine learning to play chess.