'Learning' programming assignments for the Harvard CS50's Introduction to Artificial Intelligence with Python 2021 course
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Updated
Nov 3, 2021 - Python
'Learning' programming assignments for the Harvard CS50's Introduction to Artificial Intelligence with Python 2021 course
Build A neural network that can play snack Game using (D.Q.N)
Python projects for Introduction to Artificial Intelligence course at Warsaw University of Technology.
Application of Q-Learning where robot find ways to achieve goal. This respository is intended to fulfill the task of Reinforcement learning (changing the environment of implementing Q-Learning)
Continuing the drone problem presented in https://github.com/elaysason/Deterministic-Drone-Problem but in this itertion, the drone must navigate an unknown environment under RL settings. There is no client, only a stationary place to drop the packages off.
Game Tic-Tac-Toe with Q_learning algorithm
Machine Learning project using Q-Learning without any ML library
This is a simple implementation of a game player using reinforcement-learning
A public reupload of AI Q-Learning Project
Two intelligent agents (cat and mouse) compete with each other to achieve their goal. Agents are trained through reinforcement learning (Q-learning).
Utilizando o algoritmo Q-Learning para ensinar um agente (taxi) a buscar um passageiro e deixá-lo no destino correto, passando pela menor quantidade de estados possíveis.
Open-zero is a research project aiming to realize the various projects of the company DeepMind
Q-learning algorithm is implemented to play the pacman game and get the highest possible score.
a Python-based platformer infused with Q-Learning and dynamic level creation from simple JSON files.
Six Tetris Artificial Intelligence Algorithms Showdown
Trained an artificial intelligence agent using reinforcement learning to play a simple version of the game "Snake". Implemented a Temporal Difference version of the Q-learning Algorithm. Completed for school.
This project demonstrates a reinforcement learning agent that plays the classic Snake game. The agent is trained using Q-learning to optimize its performance in the game.
This project aims to find a possible solution to a search problem in a given environment with two players using Deep Reinforcement Learning. Additionally, after one player finds the other, the task shifts to maintaining visibility for the longest possible time
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