Implementation of many popular AI algorithms to play the game of Pacman such as Minimax, Expectimax and Greedy.

The course contest involves a multi-player capture-the-flag variant of Pacman, where agents control both Pacman and ghosts in coordinated team-based strategies

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

Reinforcement Learning Course Project - IIT Bombay Fall 2018

Artificial Intelligence search algorithm base on Pacman

Implementation of BFS, DFS, A*, HillClimber, Genetic, MCTS Agents

Deep Reinforcement Learning Pacman in Pytorch

A Pacman game implementation with an AI player using the Minimax algorithm. This project showcases the classic Pacman game environment, where the player (Pacman) navigates a maze to collect points while avoiding ghosts. The AI-controlled ghosts aim to catch Pacman.

Pacman using c++ and Computer Graphics.

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

MCTS Pacman agent applied to the Pacman VS Ghosts conpetition framework

A software framework to learn algorithms like AI planning, behaviour trees, and reinforcement learning.

Implementations of artificial intelligence agents that plays Pac-Man

Partially observable PacMan game in OpenAI Gym format

💾 A python3 version of Berkeley's Pacman AI course

Pacman agent will search paths from maze to arrive at a certain destination and collect food efficiently

A repository for the Solutions for the PacMan assignment from Berkley

Exploring Learning Algorithms with Pac-Man

機械学習でパッ○マンをプレイさせる