Artificial-Intelligence-Pac-Man and Pacman-With-AI-Python
These are ecosystem siblings—both are independent implementations of AI search algorithms (likely from the same UC Berkeley CS 571 course assignment) that solve the same Pac-Man problem domain using different code bases and approaches, but neither depends on or enhances the other.
About Artificial-Intelligence-Pac-Man
iamjagdeesh/Artificial-Intelligence-Pac-Man
CSE 571 Artificial Intelligence
Implements pathfinding algorithms (DFS, BFS, A*), adversarial game-playing techniques (minimax, alpha-beta pruning), and reinforcement learning approaches (Q-learning, value iteration) across four progressive projects. The Pacman domain serves as a testbed for comparing classical search methods, multi-agent decision-making with imperfect information (HMM, particle filtering), and deep RL agents optimizing long-term utility. Built in Python 2.7 with autograded evaluation framework for each algorithmic component.
About Pacman-With-AI-Python
andi611/Pacman-With-AI-Python
Implementations of artificial intelligence agents that plays Pac-Man
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