2048 environment for Reinforcement Learning and DQN algorithm

Deep Reinforcement Learning based Decision-Making in Autonomous Driving Tasks

Deep Reinforcement Learning with Double Q-learning

This is an implementation of Deep Reinforcement Learning for a navigation task. Specifically, DQN algorithm with experience replay method is used to solve the task.

Implemented a Rainbow DQN with Prioritized Experience Replay for Atari games (Space Invaders, CartPole), achieving more efficient learning, faster convergence, and higher performance than traditional DQN.

基于DQN算法的投球2D仿真，没有考虑空气阻力，仅用于算法理解

A Streamlit application demonstrating Reinforcement Learning (RL) for intelligent product recommendations in online advertising. Explore different RL algorithms and their impact on personalization.

Hybrid Multi-Agent Simulation and Reinforcement Learning framework for financial market forecasting, featuring diverse rule-based traders and a Deep Q-Network trading agent.

A reinforcement learning project exploring different RL algorithms. Namely: QLearning, DQN, PPO, TreeQN, SAVE,

Creating a simulation where car learns to drive while minimizing the collisions through RL

# FreeHoopRLThis project uses a Deep Q-Network (DQN) algorithm to train an AI agent for shooting basketballs in a simple 2D environment. The agent learns to choose the right angle and force to score points, with results visualized through training and analysis graphs. 🎉🤖

Simple breakout game with DQN agent which learn how to play it.

This project implements a self-driving car agent using Deep Q-Network (DQN) in a simulated highway environment.

a 2D platformer game made with Unity engine and C#

First I created an environment of openAI and Gymnasium I have campared Q-Learning Algoirthm and and DQN Learning Algorithm I got best reward DQN Because It's advance

This repo hosts a sophisticated reinforcement learning setup for training a DQN agent in “CarRacing-v2”. It has self-adaptive features like dynamic learning rate and domain randomization to boost agent training and performance. It includes an Evaluation Callback for optimal model retention and leverages GPU for quicker training.

The "Reinforcement Learning Snake Game" project uses Deep Q-Learning to train an AI agent to play Snake autonomously. The agent learns to maximize its score by eating apples and avoiding collisions, demonstrating reinforcement learning in a game environment. The project includes game logic, RL agent code, and training scripts.

"Introduction to Reinforcement Learning" course at the Catholic University of Eichstätt-Ingolstadt