https://github.com/abazshoushtari/berkeley-ai-projects-pacman

AI Project Spring 2023: The Pac-Man Project from Berkeley's Intro to AI course
https://github.com/abazshoushtari/berkeley-ai-projects-pacman

Last synced: about 2 months ago
JSON representation

AI Project Spring 2023: The Pac-Man Project from Berkeley's Intro to AI course

• Host: GitHub
• URL: https://github.com/abazshoushtari/berkeley-ai-projects-pacman
• Owner: ABazshoushtari
• Created: 2024-09-11T15:20:40.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-12T21:09:39.000Z (4 months ago)
• Last Synced: 2024-09-13T10:53:41.326Z (4 months ago)
• Language: Python
• Homepage:
• Size: 5.54 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# AI Project Spring 2023: The Pac-Man Project from Berkeley's Intro to AI course

## Overview

The [Pac-Man Projects, developed at UC Berkeley,](http://ai.berkeley.edu) explore AI concepts by designing intelligent agents for the classic Pac-Man game. It serves as a hands-on platform for experimenting with search algorithms, adversarial strategies (like minimax), reinforcement learning, and probabilistic inference. These techniques allow Pac-Man to efficiently navigate mazes, evade ghosts, and maximize rewards while applying fundamental AI algorithms to real-world problems.







## Projects' description

- ### 1-Search

   Implemented 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.

- ### 2-MultiAgent

   Classic Pacman is modeled as both an adversarial and a stochastic search problem. Implemented multiagent minimax and expectimax algorithms, as well as designing evaluation functions.

- ### 3-ReinforcementLearning

   Implemented Reinforcement Learning algorithms to train agents in grid-based environments. The focus is on value iteration, Q-learning, and policy optimization techniques, applying them to solve tasks such as navigating mazes and collecting rewards.

- ### 4-GhostBusters

  Implemented probabilistic tracking algorithms to enable an agent to locate hidden objects within a grid-based environment. It focuses on techniques like Hidden Markov Models and particle filters to estimate the positions of moving targets based on uncertain and noisy sensor data.