https://github.com/hamid-rezaei/berkeley-ai-projects

Fundamental of AI course which focuses on search, multiagents, mdp and reinforcement learning algorithms.
https://github.com/hamid-rezaei/berkeley-ai-projects

adversial-search mdp python reinforcement-learning searching-algorithms

Last synced: 6 months ago
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Fundamental of AI course which focuses on search, multiagents, mdp and reinforcement learning algorithms.

• Host: GitHub
• URL: https://github.com/hamid-rezaei/berkeley-ai-projects
• Owner: Hamid-Rezaei
• Created: 2023-04-10T09:41:46.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2023-12-04T15:08:31.000Z (almost 2 years ago)
• Last Synced: 2025-04-22T10:32:30.961Z (6 months ago)
• Topics: adversial-search, mdp, python, reinforcement-learning, searching-algorithms
• Language: Python
• Homepage: https://inst.eecs.berkeley.edu/~cs188/fa23/
• Size: 2.22 MB
• Stars: 8
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# AI Project Spring 2023: The Pac-Man Projects

## Overview

The [Pac-Man Projects, developed at UC Berkeley](http://ai.berkeley.edu), aims to advance the field of artificial intelligence through the development and evaluation of intelligent agents in the context of the Pacman game. It serves as a playground for exploring different AI algorithms, including search algorithms, adversarial search, reinforcement learning, and probabilistic inference.







## Each Project Aim

- ### 1-Search

   Implement and evaluate search algorithms to enable Pacman agents to navigate the game maze effectively. This includes depth-first search, breadth-first search, uniform-cost search, and A* search.

- ### 2-MultiAgent

   Develop intelligent agents that can compete against ghosts using adversarial search algorithms. Minimax and alpha-beta pruning techniques allow agents to make optimal decisions in a competitive environment.

- ### 3-ReinforcementLearning

   Employ Markov decision processes and reinforcement learning techniques, such as Value Iteration, Q-learning, and Approximate Q-learning, to enable Pacman agents to learn and improve their performance over time. Agents can adapt and make optimal decisions by maximizing cumulative rewards.

- ### 4-GhustBusters

   Utilize probabilistic models, such as Bayesian networks and Hidden Markov Models, to reason under uncertainty and make informed decisions in complex situations.