The mario-ai project offers a comprehensive pipeline for developing AI agents to master Super Mario Bros. using neuroevolution. By integrating a Python-based NEAT implementation with the OpenAI Gym SuperMario environment, it allows users to define custom fitness criteria, mutation rates, and network topologies. During training, the framework evaluates generations of neural networks, selects high-performing genomes, and provides real-time visualization of both gameplay and network evolution. Additionally, it supports saving and loading trained models, exporting champion genomes, and generating detailed performance logs. Researchers, educators, and hobbyists can extend the codebase to other game environments, experiment with evolutionary strategies, and benchmark AI learning progress across different levels.

BomberManAI is an AI agent designed to play the classic Bomberman game autonomously. Developed in Python, it interfaces with a game environment to perceive map states, available moves, and opponent positions in real time. The core algorithm combines A* pathfinding, breadth-first search for reachability analysis, and a heuristic evaluation function to determine optimal bomb placement and evasion strategies. The agent handles dynamic obstacles, power-ups, and multiple opponents on various map layouts. Its modular architecture enables developers to experiment with custom heuristics, reinforcement learning modules, or alternative decision-making strategies. Ideal for game AI researchers, students, and competitive bot developers, BomberManAI provides a flexible framework for testing and improving autonomous gaming agents.