GSP691

Overview

Like many other areas of machine learning research, reinforcement learning (RL) is evolving at breakneck speed. Just as they have done in other research areas, researchers are leveraging deep learning to achieve state-of-the-art results.

In this lab, you will learn the basics of reinforcement learning by building a simple game.

What you'll learn

In this lab, you will:

• Understand the fundamental concepts of reinforcement learning
• Create a Vertex AI Workbench instance
• Clone the lab notebook into the Vertex AI Workbench instance
• Read, understand, and run the steps found in the notebook

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

• Time to complete the lab—remember, once you start, you cannot pause a lab.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details pane.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details pane.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Task 1. Reinforcement learning 101

Reinforcement learning (RL) is a form of machine learning whereby an agent takes actions in an environment to maximize a given objective (a reward) over this sequence of steps. Unlike more traditional supervised learning techniques, every data point is not labelled and the agent only has access to "sparse" rewards.

While the history of RL can be dated back to the 1950s and there are a lot of RL algorithms out there, 2 easy to implement yet powerful deep RL algorithms have a lot of attractions recently: deep Q-network (DQN) and deep deterministic policy gradient (DDPG). We briefly introduce the algorithms and variants based on them in this section.

A conceptual process diagram of the Reinforcement Learning problem

The Deep Q-network (DQN) was introduced by Google DeepMind's group in this Nature paper in 2015. Encouraged by the success of deep learning in the field of image recognition, the authors incorporated deep neural networks into Q-Learning and tested their algorithm in the Atari Game Engine Simulator, in which the dimension of the observation space is very large.

The deep neural network acts as a function approximator that predicts the output Q-values, or the desirability of taking an action, given a certain input state. Accordingly, DQN is a value-based method: in the training algorithm DQN updates Q-values according to Bellman's equation, and to avoid the difficulty of fitting a moving target, it employs a second deep neural network that serves as an estimation of target values.

On a more practical level, the following model highlights the source files, the shell command, and the endpoint to get an RL job running on Google Cloud:

Task 2. Create a Vertex AI Workbench instance

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI.

2. Click Enable All Recommended APIs.

3. On the left-hand side, click Workbench.

4. At the top of the Workbench page, ensure you are in the Instances view.

5. Click Create New.

6. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size)

7. Click Create.

8. Click Open JupyterLab next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

9. Click the Terminal icon to open a terminal window.

Your terminal window will open in a new tab. You can now run commands in the terminal to interact with your Workbench instance.

Click Check my progress to verify the objective. Create a Vertex AI Workbench instance

Task 3. Copy the sample code

1. Copy and run the following code in the terminal to copy the notebook file .

2. From the left-hand menu, select early_rl > . This will open a new tab.

Click Check my progress to verify the objective. Copy the sample code

Task 4. Run through the notebook

Your new tab should look similar to the following:

1. Read through the following notebook and run all code blocks with Shift + Enter.

2. Return here after you have completed the instructions in the notebook.

Congratulations!

In this lab you learned the basic principles of reinforcement learning (RL). After creating a Jupyterlab instance, you cloned a sample repository and ran through a notebook where you received hands-on practice with the fundamentals of reinforcement learning. You are now ready to take more labs in this series.

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Manual Last Updated November 27, 2024

Lab Last Tested November 25, 2024

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