AlphaZero-Edu: Making AlphaZero Accessible to Everyone
It provides an accessible and practical benchmark for academic and industrial applications, though it is incremental as it builds upon the existing AlphaZero framework.
The paper tackles the high complexity and poor reproducibility of existing reinforcement learning frameworks by introducing AlphaZero-Edu, a lightweight, education-focused implementation that achieves a 3.2-fold speedup with 8 processes and demonstrates high win rates in Gomoku matches.
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.