SEMLA Tutorial: Debugging DRL Systems with RLExplorer

Welcome to the tutorial on debugging Deep Reinforcement Learning (DRL) systems using RLExplorer, a powerful tool for fault detection in DRL applications. This tutorial is designed to provide you with a hands-on experience in identifying and fixing bugs in DRL algorithms. In this tutorial, we will mostly be working with two Google Colaboratory files:

1. Colab 1: Demo: https://colab.research.google.com/drive/1PdUrqgKE9YUGK5_2pDR3IGpw4x51BCqq?usp=sharing
2. Colab 2: Buggy Sample: https://colab.research.google.com/drive/1qmu7udtM4r1osgvUOraw2iabgvt2LtEJ?usp=sharing

Table of Contents

1. Introduction to Deep Reinforcement Learning (DRL)
2. Challenges of Monitoring DRL
3. RLExplorer: Fault Detection Tool for DRL Systems
4. Integrating RLExplorer into a DRL Application
5. Demo: Fault Detection using RLExplorer

Link to RLEXplorer repo

https://github.com/rached1997/RLDebugger

1. Introduction to Deep Reinforcement Learning (DRL)

In this section, we will provide a brief overview of Deep Reinforcement Learning (DRL) and its application areas. We will discuss the fundamental concepts, algorithms, and the underlying principles that make DRL a powerful approach for solving complex problems.

While we won't dive into intricate technical details, this section aims to familiarize you with the fundamental concepts and terminology used in DRL. This foundational knowledge will enable you to follow the subsequent sections of the tutorial and engage in hands-on tasks with RLExplorer.

2. Challenges of Monitoring DRL

Monitoring and debugging DRL algorithms pose unique challenges compared to traditional software applications. In this section, we will explore these challenges, including the non-deterministic nature of DRL, the exploration-exploitation trade-off, and the difficulties in diagnosing and fixing bugs in DRL systems.

3. RLExplorer: Fault Detection Tool for DRL Systems

RLExplorer is a fault detection tool specifically designed for DRL applications. This section will introduce you to RLExplorer and its capabilities. You will learn how RLExplorer leverages advanced techniques to identify and diagnose bugs in DRL algorithms, providing valuable insights into their behavior and performance.

4. Integrating RLExplorer into a DRL Systems

In this tutorial section, we will guide you through the process of integrating RLExplorer into your own DRL application. You will learn the necessary steps to configure and instrument your code to enable fault detection using RLExplorer. We will provide code examples and practical tips to help you seamlessly integrate RLExplorer into your development workflow.

We will start with a "Hello World" example of a dummy DRL application. This example will demonstrate how easy it is to integrate RLExplorer into your codebase using just three simple steps. By following along with the provided code and instructions in Colab-1, you will gain hands-on experience in incorporating RLExplorer and setting up fault detection in your DRL system.

This interactive exercise aims to showcase the simplicity and effectiveness of RLExplorer's integration process.

5. Demo: Fault Detection using RLExplorer

In the final section of the tutorial, we will showcase a live demo of fault detection using RLExplorer. We will use a concrete example of a DQN algorithm implementation aimed at solving the cartPole environment. The code for this demo has been adapted from the implementation provided on the PyTorch website.

In this interactive session, we have intentionally injected five real-world bugs that developers often make in DRL applications. Through the demo, you will witness how RLExplorer efficiently identifies and reports these bugs. We will guide you through the debugging process, discussing the valuable insights provided by RLExplorer and the steps taken to fix the identified issues.

To participate in the demo and gain hands-on experience in fault detection using RLExplorer, please access the Colab-2. This Colab contains the necessary code, instructions, and explanations to follow along with the demo.

By engaging in this practical exercise, you will witness the power of RLExplorer in effectively detecting and diagnosing bugs in a real-world DRL algorithm implementation, allowing you to apply these insights to your own projects.

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By the end of this tutorial, you will have gained a solid understanding of DRL, the challenges involved in monitoring DRL systems, and how to effectively utilize RLExplorer for fault detection in DRL applications. You will be equipped with practical knowledge and hands-on experience to improve your own DRL algorithms.

We hope you find this tutorial engaging and insightful. Let's dive into the world of DRL debugging with RLExplorer!

PS: Text written with the help of OpenAI ChatGPT 😄