Monte Carlo tree search library for general problems
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Updated
Feb 12, 2018 - C++
Monte Carlo tree search library for general problems
A variation of Pacman arcade game designed to train Pacman agents that use sensors to locate and eat invisible ghosts with phenomenal efficiency. Used Joint Particle Filter algorithm in AI to get 30% optimized results.
Implementations for Ms. PACMAN
I.A. utilizando estratégias de busca ("Expectiminimax" e "Monte-Carlo tree search") para jogar dominó com dois jogadores.
A python implementation of an agent for ultimate tic-tac-toe using Monte Carlo Tree Search and Upper Confidential Bound
An implementation of DQN and Monte Carlo Tree Search to play the game of Texas Hold'em
A CLI connect 4 game, with a simple Monte Carlo tree search implementation
A connect four game with AI built using Monte Carlo Tree Search with UCT applied to balance exploration/exploitation. There are 2 versions of the application: One designed to be used in the terminal and one for an Arduino with a tft display
Implementation of Monte-Carlo Tree Search for the game of Gomoku
A Monte-Carlo Tree Search AI for the Pentago Twist board game.
In this project I created a 4x4x4 Tic Tac Toe as well as 3 agents that one can play against.
Feature Acquisition Project
Designed Pacman game with controls & UI | C# | Unity game engine
Group project on Adversarial Search Algorithms for the Curricular Unit of "Artificial Intelligence" @ FCUP, Porto with @barbara-san and @Nia3324
Docker files for connecting the PROST planner with pyRDDLGym.
A high performance Ultimate Tic-Tac-Toe engine in the browser
Connect 4 with AI powered by Monte Carlo Tree Search.
An ISMCTS AI for the card game Schnapsen
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