Pacman AI offers a fully functional Python-based environment and agent framework for the classic Pacman game. The project implements key reinforcement learning algorithms—Q-learning and value iteration—to allow the agent to learn optimal policies for pill collection, maze navigation, and ghost avoidance. Users can define custom reward functions and adjust hyperparameters such as learning rate, discount factor, and exploration strategy. The framework supports metric logging, performance visualization, and reproducible experiment setups. It is designed for easy extension, letting researchers and students integrate new algorithms or neural network-based learning approaches and benchmark them against baseline grid-based methods within the Pacman domain.

GameGen-X is a cutting-edge AI tool that utilizes a two-phase training process to generate engaging open-world video games. The first phase, Foundation Pretraining, involves training the model on the OGameData dataset using text-to-video generation and video continuation techniques. The second phase, Instruction Tuning, fine-tunes the model with InstructNet to enable real-time interactive content generation. Powered by over 32,000 curated game footage clips from RPGs, FPS, racing games, and more, GameGen-X allows users to produce diverse and immersive gaming environments effortlessly.

AIpacman is an open-source Python project that simulates the Pac-Man game environment for AI experimentation. Users can choose from built-in agents or implement custom ones using search algorithms like DFS, BFS, A*, UCS; adversarial methods such as Minimax with Alpha-Beta pruning and Expectimax; or reinforcement learning techniques like Q-Learning. The framework provides configurable mazes, performance logging, visualization of agent decision-making, and a command-line interface for running matches and comparing scores. It is designed to facilitate educational lessons, research benchmarks, and hobbyist projects in AI and game development.