Welcome to this repository showcasing the implementation and analysis of various reinforcement learning (RL) algorithms. This project demonstrates the application of RL techniques to both simple and complex scenarios, highlighting the versatility and power of these methods in solving diverse problems.
Project Overview This repository contains two main components:
Basic Task: Q-Learning in a House Cleaning Scenario Implementation of Q-learning in a simplified house cleaning environment Analysis of parameter sensitivity and convergence behaviors Demonstration of how basic RL can be applied to everyday task optimization
Advanced Task: Deep Q-Network (DQN) and Its Variants Implementation of DQN and Double DQN with prioritized experience replay Application to more complex environments for reward maximization Comparison of performance between different DQN variants Through these implementations, we explore the progression from tabular methods to deep learning-based approaches in reinforcement learning. The project aims to provide insights into the practical applications of RL in both simulated environments and potential real-world scenarios.
Dive into the code to see how these algorithms are implemented, and check out the analysis to understand their performance characteristics and potential applications.