Implementation of Deep Q Learning on solving the honeypot placement problem.

Les intelligences artificielles

Implement Deep Q Learning to train agent for Lunar Landing

repo for learning reinforcement learning from scratch

reinforcement learning applied to simple board games

This project provides a comprehensive understanding of reinforcement learning, focusing on Deep Q-Learning (DQN). It involves exploring the OpenAI Gym library, implementing DQN from DeepMind's seminal paper, and enhancing the DQN algorithm for improved performance and stability.

Try to reproduce basic example of Deep Q Learning (DQN) with Pytorch

A transformer-based deep RL trading bot built with PyTorch.

DQN Based Atari Training Application

CS 5180 Reinforcement Learning: Final Project

Scratch solution to the OpenAI Discrete Lunar Lander Environment using Deep Q-Learning.

Lunar Lander training using Deep-Q-Learning

Reinforcement learning for playing flappy bird game

Deep Q-Learning consists of combining Q-Learning with Artificial Neural Networks. Inputs are encoded vectors, each one defining a state of the environment. These inputs go to an Artificial Neural Network, where the output is the action to play

Implementation of the Double Deep Q-Learning algorithm with a prioritized experience replay memory to train an agent to play the minichess variante Gardner Chess

The code release of "Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning" paper, ICAART 2021

🤖 A deep Q-learning AI model trained with TensorFlow in Python powers a 3D Connect 4 web application.

This repo implements Deep Q-Network (DQN) for solving the Frozenlake-v1 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 in both 4x4 and 8x8 map sizes.

This repo implements Deep Q-Network (DQN) for solving the Cliff Walking v0 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 with the finest tuning.

This repo implements Deep Q-Network (DQN) for solving the Mountain Car v0 environment (discrete version) of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 with a custom reward function for faster convergence.