This repository contains my implementations of three Pacman projects assigned as part of my coursework at the University of Athens during my junior year (YS02). These projects are adapted from the original Pacman projects offered in the CS188 course at the University of California, Berkeley. Each project delves into different aspects of artificial intelligence, exploring a range of techniques to control the behavior of Pacman and other agents in the game.
The repository is organized into three folders, each representing a project. Each folder contains the files I changed to solve the problems needed. If you would like to run the code, download the rest of the files for each project (Links given below):
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Project 1: Search Algorithms - Files
- Implement basic search algorithms to find optimal paths for Pacman through mazes.
- Explore uninformed search algorithms like Depth-First Search and Breadth-First Search.
- Apply informed search algorithms like A* Search with different heuristics.
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Project 2: Multi-Agent Search - Files
- Design adversarial agents to compete against each other in Pacman games.
- Implement adversarial search algorithms such as Minimax and Alpha-Beta Pruning.
- Analyze the performance of different evaluation functions for estimating game states.
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Project 3: Reinforcement Learning - Files
- Train Pacman to make intelligent decisions using reinforcement learning.
- Implement algorithms like Q-Learning and Approximate Q-Learning.
- Explore the impact of feature design and parameter tuning on Pacman's learning.
To get started with these projects, follow the instructions provided in each project folder. Each project contains its own set of code files, instructions, and additional resources to help you understand and implement the required functionality.
If you need additional information or resources related to the Pacman projects, you can mail me or refer to the official CS188 course materials from UC Berkeley: