2048 environment for Reinforcement Learning and DQN algorithm
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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.
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.
A reinforcement learning project exploring different RL algorithms. Namely: QLearning, DQN, PPO, TreeQN, SAVE,
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 collection of automated trading bots built with Python, including pair trading, Q-learning, and DQN strategies.
Creating a simulation where car learns to drive while minimizing the collisions through RL
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)
a 2D platformer game made with Unity engine and C#
This project implements a self-driving car agent using Deep Q-Network (DQN) in a simulated highway environment.
# 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. 🎉🤖
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.
Implementations of some of the most well known Deep Reinforcement Learning algorithms
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