There are 1 repository under q-learning-algorithm topic.
Tabular methods for reinforcement learning
PyTorch implementation of the Q-Learning Algorithm Normalized Advantage Function for continuous control problems + PER and N-step Method
This repository contains the code for automatically generating piano fingerings using a reinforcement learning agent that uses Q-Learning.
Open-zero is a research project aiming to realize the various projects of the company DeepMind
The objective is to teach robot to find and reach the target object in the minimum number of steps and using the shortest path and avoiding any obstacles such as humans, walls, etc usinf reinforcement learning algorithms.
Turn based strategy game with AI
Q-learning application to find an optimal parking slot
Two intelligent agents (cat and mouse) compete with each other to achieve their goal. Agents are trained through reinforcement learning (Q-learning).
The implementation for the paper Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis // NeurIPS 2022
a Python-based platformer infused with Q-Learning and dynamic level creation from simple JSON files.
Deep Q Learning blackbox strategies for casino games
This repository contains a Jupyter Notebook with an implemenation of a Q-Learning Agent, which learns to solve the n-Chain OpenAI Gym environment
🕹️ Welcome to Game-Optimization, 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.
Docking robot in a grid environment trained it with Q-learning
Demonstration of Q-Learning and SARSA algorithms utilizing Python and OpenAI GYM
Build an RL (Reinforcement Learning) agent that learns to play Numerical Tic-Tac-Toe. The agent learns the game by Q-Learning.
This repository contains various networks implementation such as MLP, Hopfield, Kohonen, ART, LVQ1, Genetic algorithms, Adaboost and fuzzy-system, CNN with python.
A collaborative repository for our Bachelor's thesis, focused on optimizing the Cell Outage Compensation (COC) algorithm in Self-Organizing Networks (SONs). Leveraging AI-Hardware Acceleration, the project aims to bolster 5G network reliability, particularly for emerging technologies like autonomous driving.
The 3D bin packing problem is a combinatorial optimization problem that involves fitting a given set of items of various sizes into a container of a specific size such that the total volume of the items is as close to the volume of the container as possible.
The content of this repository will be inherent to the Computational Intelligence course at Polytechnic University of Turin academic year 2023/2024
Dynamic Q-Learning Based Feature Selection approach
Q-Learning applied to Gymnasium's Toy Text environments: FrozenLake, CliffWalking, BlackJack, and Taxi.
SUTD 50.021 Artificial Intelligence Project - Wordle Solver using Reinforcement Learning
using Rl an agent learns how much insulin to pump.
In this project, we tried two different Learning Algorithms for Hierarchical RL on the Taxi-v3 environment from OpenAI gym. SMDP Q-Learning and Intra Option Q-Learning and contrasted them with two other methods that involve hardcoding based on human understanding. We conclude that the solutions learnt by machine are way superior than humans for this problem. Intra Option Q-Learning outperforms SMDP Q-Learning because of better usage of the SARS samples (similar to experience replay). Our algorithms even outperform the Hardcoded Agent. We also demonstrated and concluded the strong effectiveness of state compression on the model performance.
Tic-Tac-Toe Q-Learning is a beginner-friendly example of using reinforcement learning.
Made with the gym package from the farama foundation, this project is an hyper detailed version of the Q-Learning reinforcement on the Frozen lake's game.
Playing games using Reinforcement Learning. As part of the MITx course on machine learning with Python - from linear models to deep learning
An interactive JavaScript simulation of ant colonies using reinforcement learning (Q-learning), showcasing real-time foraging, pheromone trails, and emergent swarm intelligence.
This project is a simplified approach to algorithmic trading and provides insights into the application of reinforcement learning in financial markets. It can serve as a foundational step towards building more sophisticated trading strategies using machine learning and artificial intelligence.
This project serves as an introduction to Deep Q-Learning and reinforcement learning concepts. The trained agent learns to balance the cart-pole system through iterative training and evaluation. You can modify the environment or parameters to further experiment with different reinforcement learning strategies.
C++ program implements Sokoban game to solve it with AI reinforcement algorithm (Q-learning ALG).
C++ implementation of the Q-Learning algorithm applied to a simple grid world problem
Porjects for Algorithmic Techniques for Artificial Intelligence