2048 environment for Reinforcement Learning and DQN algorithm
-
Updated
May 27, 2022 - Python
2048 environment for Reinforcement Learning and DQN algorithm
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.
A reinforcement learning project exploring different RL algorithms. Namely: QLearning, DQN, PPO, TreeQN, SAVE,
# 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. 🎉🤖
This project implements a self-driving car agent using Deep Q-Network (DQN) in a simulated highway environment.
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
Implementations of some of the most well known Deep Reinforcement Learning algorithms
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.
A small demo of RL algorithms on a trash collector robot
Reinforcement Learning: Cartpole Balancing with a DQN Agent
🏀 Train an AI agent to master basketball shooting using a deep Q-network in a 2D environment, enhancing algorithm understanding and skill development.
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."