I Built an AI Arena and Trained AlphaZero to Play Gomoku: Here’s How

Building a board game AI is a fantastic way to dive into Reinforcement Learning and search algorithms. But once you've built your AI, a new problem arises: How do you actually test it against other algorithms? If you write a classic Minimax agent in Java and an AlphaZero model in Python, how do you make them fight? To solve this, I built a two-part ecosystem: Gomoku Battle : A cross-language, cross-system arena for AI agents. AlphaZero Board Games : A lightweight, readable AlphaZero implementation trained to dominate the arena. Here is a deep dive into the architecture of both projects and how they work together. 🏟️ Part 1: The Arena (Gomoku Battle) GitHub: zhixiangli/gomoku-battle The goal of gomoku-battle was to create a pluggable, language-agnostic referee system. I wanted to be able to write an AI in any language, plug it into the arena, and watch it play in a UI. The Architecture I built the platform using Java, splitting it into specialized modules: gomoku-battle-core : Handles t