[2504.14636] AlphaZero-Edu: Democratizing Access to AlphaZero

Computer Science > Machine Learning arXiv:2504.14636 (cs) [Submitted on 20 Apr 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:AlphaZero-Edu: Democratizing Access to AlphaZero Authors:Ruitong Li, Aisheng Mo, Guowei Su, Ru Zhang, Binjie Guo, Haohan Jiang, Xurong Lin, Hongyan Wei, Jie Li, Zhiyuan Qian, Zhuhao Zhang, Xiaoyuan Cheng View a PDF of the paper titled AlphaZero-Edu: Democratizing Access to AlphaZero, by Ruitong Li and 11 other authors View PDF Abstract:Recent years have witnessed significant progress in reinforcement learning, especially with Zero-like paradigms, which have greatly boosted the generalization and reasoning abilities of large-scale language models. Nevertheless, existing frameworks are often plagued by high implementation complexity and poor reproducibility. To tackle these challenges, we present AlphaZero-Edu, a lightweight, education-focused implementation built upon the mathematical framework of AlphaZero. It boasts a modular architecture that disentangles key components, enabling transparent visualization of the algorithmic processes. Additionally, it is optimized for resource-efficient training on a single NVIDIA RTX 3090 GPU and features highly parallelized self-play data generation, achieving a 3.2-fold speedup with 8 processes. In Gomoku matches, the framework has demonstrated exceptional performance, achieving a consistently high win rate against human opponents. AlphaZero-Edu has been open-sourced at this https URL, providing ...