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A rust implementation of the famous 2048 game
CSE 571 Artificial Intelligence
A set of AIs for the 2048 tile-merging game. Includes an expectimax strategy that reaches 16384 with 34.6% success and an ML model trained with temporal difference learning.
A simplified version of Go game in Python, with AI agents built-in and GUI to play.
Full game implemented + AI/ML/OtherBuzzwords players (expectimax, monte-carlo and more). Fork me!
Implementation of many popular AI algorithms to play the game of Pacman such as Minimax, Expectimax and Greedy.
Solutions for the Projects of the Artificial Intelligence (CS 188) course of UC Berkeley
Bots for the board game quoridor implemented using four algorithms: minimax, minimax with alpha beta pruning, expectimax and monte carlo tree search.
一个100KB的内置强大AI的安卓2048小游戏。每秒5次移动的情况下,AI有94%概率合成16384。A 100KB android 2048 game with a powerful AI !!!
My solutions to projects 1, 2 & 3 of Berkeley's AI course
2048 game solved with Expectimax
Pokémon battles simulator, with the use of MiniMax-Type algorithms (Artificial Intelligence project)
Variance of the board game Settlers of Catan, with a University/Campus theme
A multi-agent implementation of the game Connect-4 using MCTS, Minimax and Exptimax algorithms.
Solutions to Pacman AI Multi-Agent Search problems
A 2048 AI, written in C++ using an ASCII interface and the Expectimax algorithm.
Final project of the course Introduction to Artificial Intelligence of NCTU
Collection of AI algorithms to solve/optimize 2048 games.
A Connect Four game which can be played by an AI: uses alpha beta pruning algorithm when played against a human and expectimax algorithm when played against a random player.
UC Berkeley CS188: Artificial Intelligence
Some little games implementation, and also, machine learning implementation.
Implementation of reinforcement learning algorithms to solve pacman game. Part of CS188 AI course from UC Berkeley.
Contains a series of mini-projects based on UC Berkeley Pacman Projects & UArizona Hunt The Wumpus Project
🤖 2048 AI that reaches the 16,384 Tile
The phase 2 of my AI project, which is adversarial search in Pacman game for reaching the best utility and avoiding ghosts. Minimax with alpha-beta pruning and Expectimax is implemented.
UC Berkeley CS188 Intro to AI -- Pacman Project Solutions
2048 Pseudo AI | 15-112 Term Project (Spring 2019)
Business War Policy Exploration-AI project for CS181 in Shanghaitech
Computer Graphics and AI (ML DL included) compilations.
Pacman game and AI agents
:ghost: :video_game: This is my implementation in the famous Berkeley pacman artificial intelligence project: http://ai.berkeley.edu/project_overview.html.
Adversarial search algorithms, including Minimax, Alpha-Beta Pruning, and Expectimax, to create an agent that competes against another in a map-based game environment.