🤖Artificial Intelligence - Berkeley - CS188 - Summer 2016

This is a repository for me to record my notes of cs188

Reinforcement Learning in Pacman

My solutions to projects 1, 2 & 3 of Berkeley's AI course

Pacman AI Projects 1,2,3 - UC Berkeley

Shanghaitech CS181

Implementation of projects 0,1,2,3 of Berkeley's AI course

Explore foundational AI concepts through the Pac-Man projects, designed for UC Berkeley's CS 188 course. Implement search algorithms, multi-agent strategies, and reinforcement learning techniques in Python, emphasizing real-world applications. Engage in the Eutopia Pac-Man contest for a multiplayer capture-the-flag challenge

👻 UC Berkeley CS188 Intro to AI -- The Pac-Man Projects

In this project, you will design agents for the classic version of Pacman, including ghosts. Along the way, you will implement both minimax and expectimax search and try your hand at evaluation function design.

The Pac-Man Projects from UC Berkeley Introduction to Artificial Intelligence course (CS188)

I have completed four Pacman projects of the UC Berkeley CS188 Intro to Artificial Intelligence course. The Pac-Man projects were developed for CS 188. They apply an array of AI techniques to playing Pac-Man. They teach foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning.

Solutions for the Projects of the Artificial Intelligence (CS 188) course of UC Berkeley

In this repository, I outline the course lab carried out for the Artificial Intelligence CSE 4617 course along with the lab CSE 4618, conducted by Bakhtiyar Hasan Sir Note: We did not have to implement all of the code but rather portions of the code as outlined by the taskbook in the resources section

Implementation of different graph search algorithms in Python

UC Berkeley CS188: Introduction to Artificial Intelligence

Artificial Intelligence, UC Berkeley's CS188 taught at ShanghaiTech

Project 1 from CS 188 course concerning search algorithms.

UC Berkeley CS188

In this project, your Pacman agent will find paths through his maze world, both to reach a particular location and to collect food efficiently. You will build general search algorithms and apply them to Pacman scenarios.