AlphaZero-Edu: Making AlphaZero Accessible to Everyone

• URL: http://arxiv.org/abs/2504.14636v1
• Date: Sun, 20 Apr 2025 14:29:39 GMT
• Title: AlphaZero-Edu: Making AlphaZero Accessible to Everyone
• Authors: Binjie Guo, Hanyu Zheng, Guowei Su, Ru Zhang, Haohan Jiang, Xurong Lin, Hongyan Wei, Aisheng Mo, Jie Li, Zhiyuan Qian, Zhuhao Zhang, Xiaoyuan Cheng,
• Abstract summary: We present AlphaZero-Edu, a lightweight, education-focused implementation built upon the mathematical framework of AlphaZero.<n>It boasts a modular architecture that disentangles key components, enabling transparent visualization of the algorithmic processes.<n>In Gomoku matches, the framework has demonstrated exceptional performance, achieving a consistently high win rate against human opponents.
• Score: 4.520853683436092
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/StarLight1212/AlphaZero_Edu, providing an accessible and practical benchmark for both academic research and industrial applications.

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