joy-liningqiao / uc-berkeley-ai-pacman-project

Artificial Intelligence project designed by UC Berkeley. Designed game agents for the game Pacman using basic, adversarial and stochastic search algorithms, and reinforcement learning concepts

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PacmanAI

Project Link : http://ai.berkeley.edu/project_overview.html

Sections Of the Project Covered are:

Search: Implement depth-first, breadth-first, uniform cost, and A* search algorithms. These algorithms are used to solve navigation and traveling salesman problems in the Pacman world.

Multi-Agent Search: Classic Pacman is modeled as both an adversarial and a stochastic search problem. Implement multiagent minimax and expectimax algorithms, as well as designing evaluation functions.

Reinforcement Learning: Implement model-based and model-free reinforcement learning algorithms, applied to the AIMA textbook's Gridworld, Pacman, and a simulated crawling robot.

Ghostbusters: Probabilistic inference in a hidden Markov model tracks the movement of hidden ghosts in the Pacman world. Implement exact inference using the forward algorithm and approximate inference via particle filters.

UC-Berkeley-AI-Pacman-Project

Artificial Intelligence project designed by UC Berkeley to develop game agents for Pacman using search algorithms and reinforcement learning

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