2048 environment for Reinforcement Learning and DQN algorithm

Deep Reinforcement Learning with Double Q-learning

Deep Reinforcement Learning based Decision-Making in Autonomous Driving Tasks

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.

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

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.

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

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.

Reinforcement Learning: Cartpole Balancing with a DQN Agent

Implementations of some of the most well known Deep Reinforcement Learning algorithms

This repository contains a comprehensive implementation of a Deep Q-Network (DQN) to train an AI agent to play Atari's Breakout game. The implementation leverages OpenAI Gym for the game environment and TensorFlow/Keras for the neural network. Features include experience replay, target networks, and game monitoring via exported videos.