Welcome to AI-GameOptimization, a repository dedicated to exploring and implementing various optimization algorithms to solve complex games. This project initially focuses on solving the classic game Sokoban using the Q-learning algorithm, with plans to extend to genetic algorithms and other optimization techniques in the future.

repo for learning reinforcement learning from scratch

Several RL-agents are tested on classical environments and benchmarked against their stable-baselines implementation.

Implementation of "Augmenting Decision with Hypothesis in Reinforcement Learning"

Robot navigation with Q learning

In questa repository una collezione di tutorial sulle basi del Reinforcement Learning, sviluppati in Python, interamente in italiano.

Reinforcement Learning Short Course

🧨 Interactive temporal difference algorithm simulator in which agent has to find the optimal path to reach certain destination.

This project applies a Q-learning agent to develop a trading strategy that maximizes profit through stock trading. The environment is based on historical stock prices of Nvidia over the past two years, containing 504 entries from 02/01/2021 to 01/31/2023.

📚 List of Top-tier Conference Papers on Reinforcement Learning (RL)，including: NeurIPS, ICML, AAAI, IJCAI, AAMAS, ICLR, ICRA, etc.

This Tower of Hanoi Game Prototype is an attempt at explaining Q-learning and Reinforcement Learning Principles to People in an Intuitive and Interactive Way

Reinforcement learning used in the game of pong

This project builds a game bot for Chrome Dino Game using q-learning algorithm.

This repository explores the application of three reinforcement learning algorithms—Deep Q-Networks (DQN), Double Deep Q-Networks (DDQN), and Proximal Policy Optimization (PPO)—for playing Super Mario Bros using the OpenAI Gym and nes-py emulator. It includes a comparative analysis of these models.

Q-learning project where an agent learns by himself to find the exit inside a maze. The project is implemented as a level-based game.

implementations of basic RL algorithms

Author Implementation of "Augmenting Decision with Hypothesis in Reinforcement Learning"

This project simulates a virtual war between n RL-reasoning (Q-learning) agents.

Библиотека для создания ИИ, написана ПОЛНОСТЬЮ с нуля

We investigate the (deep) Q-learning algorithm on different environments and measure the performance of our agents.