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Reinforcement Learning: Qwik Start

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Reinforcement Learning: Qwik Start

1 hour 1 Credit

GSP691

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Overview

Like many other areas of machine learning research, reinforcement learning (RL) is evolving at breakneck speed. Researchers are leveraging deep learning to achieve state-of-the-art results.

Reinforcement learning has significantly outperformed prior ML techniques in game playing, reaching human-level and even world-best performance on Atari, beating the human Go champion, and is showing promising results in more difficult games like Starcraft II.

In this lab, you will learn the basics of reinforcement learning by building a simple game, which has been modelled on a sample provided by OpenAI Gym.

Objectives

In this lab, you will:

  • Understand the fundamental concepts of reinforcement learning.

  • Create an AI Platform Tensorflow 2.1 Notebook.

  • Clone the sample repository from the training data analyst repo found on Github.

  • 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 will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that 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).
Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.
  • Time to complete the lab---remember, once you start, you cannot pause a lab.
Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

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 pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

    • The Open Google 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 Console. 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 from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
  5. 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 Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

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.

8c84b4dbb56d882e.png

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:

62687b03fa144178.png

Create a Vertex AI Platform Notebook

To create and launch a Vertex AI Workbench notebook:

  1. In the Navigation Menu Navigation menu icon, click Vertex AI > Workbench.

  2. On the Workbench page, click New Notebook.

  3. In the Customize instance menu, select TensorFlow Enterprise and choose the latest version of TensorFlow Enterprise 2.x (with LTS) > Without GPUs.

  4. Name the notebook.

  5. Set Region to us-east1 and Zone to any zone within the designated region.

  6. In the Notebook properties, click the pencil icon pencil icon to edit the instance properties.

  7. Scroll down to Machine configuration and select e2-standard-2 for Machine type.

  8. Leave the remaining fields at their default and click Create.

After a few minutes, the Workbench page lists your instance, followed by Open JupyterLab.

  1. Click Open JupyterLab to open JupyterLab in a new tab.

Click Check my progress to verify the objective. Create a Vertex AI Platform Notebook

Clone the sample code

To clone the training-data-analyst repository in your JupyterLab instance:

  1. In JupyterLab, click the Terminal icon to open a new terminal.

Open Terminal

  1. At the command-line prompt, type the following command and press ENTER:

git clone https://github.com/GoogleCloudPlatform/training-data-analyst
  1. To confirm that you have cloned the repository, in the left panel, double click the training-data-analyst folder to see its contents.

Files in the training-data-analyst directory

Wait for this command to propagate.

Select training-data-analyst> quests> rl > early_rl > early_rl.ipynb.

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

Run through the notebook

Your new tab should look similar to the following:

6827f6f1a1eae75.png

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

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.

Finish Your Quest

This self-paced lab is part of the Baseline: Data, ML, AI quest. A quest is a series of related labs that form a learning path. Completing this quest earns you the badge above, to recognize your achievement. You can make your badge (or badges) public and link to them in your online resume or social media account. Enroll in this Quest and get immediate completion credit if you've taken this lab.

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Manual Last Updated September 22, 2021

Lab Last Tested September 22, 2021

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