Azul Game AI Agent is a specialized AI solution for the Azul board game competition. Implemented in Python, it models game state, applies Minimax search for deterministic pruning, and leverages Monte Carlo Tree Search to explore stochastic outcomes. The agent uses custom heuristics to evaluate board positions, prioritizing tile placement patterns that yield high points. It supports head-to-head tournament mode, batch simulations, and result logging for performance analysis. Users can tweak algorithm parameters, integrate with custom game environments, and visualize decision trees to understand move selection.

AI Agents for Rock Paper Scissors is an open-source Python project that demonstrates how to build, train, and evaluate different AI strategies—random play, rule-based pattern recognition, and reinforcement learning (Q-learning)—in the classic Rock-Paper-Scissors game. It provides modular agent classes, a configurable game runner, performance logging, and visualization utilities. Users can easily swap agents, adjust learning parameters, and explore AI behavior in competitive scenarios.

Colosseum Agent Battles provides a modular Python SDK for constructing AI agent competitions in customizable arenas. Users can define environments with specific terrain, resources, and rulesets, then implement agent strategies via a standardized interface. The framework manages battle scheduling, referee logic, and real-time logging of agent actions and outcomes. It includes tools for running tournaments, tracking win/loss statistics, and visualizing agent performance through charts. Developers can integrate with popular machine learning libraries to train agents, export battle data for analysis, and extend referee modules to enforce custom rules. Ultimately, it streamlines the benchmarking of AI strategies in head-to-head contests. It also supports logging in JSON and CSV formats for downstream analytics.