2048 environment for Reinforcement Learning and DQN algorithm
-
Updated
May 27, 2022 - Python
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.
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
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.
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.
Creating a simulation where car learns to drive while minimizing the collisions through RL
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
Simple breakout game with DQN agent which learn how to play it.
a 2D platformer game made with Unity engine and C#
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.
Exploring the fundamentals of reinforcement learning (RL) to build agents capable of navigating complex real-world environments and enhancing the training of large language models (LLMs)
"Introduction to Reinforcement Learning" course at the Catholic University of Eichstätt-Ingolstadt
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.
Add a description, image, and links to the dqn-algorithm topic page so that developers can more easily learn about it.
To associate your repository with the dqn-algorithm topic, visit your repo's landing page and select "manage topics."