MultiAgentPacman offers a Python-based game environment where users can implement, visualize, and benchmark multiple AI agents in the Pacman domain. It supports adversarial search algorithms like minimax, expectimax, alpha-beta pruning, as well as custom reinforcement learning or heuristic-based agents. The framework includes a simple GUI, command-line controls, and utilities to log game statistics and compare agent performance under competitive or cooperative scenarios.

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.