OpenAI Gym is a widely-used toolkit designed for developing and comparing reinforcement learning (RL) algorithms. It provides a diverse range of environments, from simple tasks like balancing a cart on a pole to complex simulations involving robotic control. These environments offer standardized challenges, making it easy to benchmark and evaluate the performance of various RL models. OpenAI Gym is flexible and allows researchers and developers to build, train, and fine-tune reinforcement learning agents, encouraging experimentation across different problem spaces. It also supports interoperability with various libraries and frameworks, making it highly adaptable for RL workflows.

NumPy is a cornerstone in the Python ecosystem, specifically geared toward numerical computing. It provides high-performance array structures and a vast collection of mathematical functions, enabling efficient operations on large datasets or simulations. In the context of reinforcement learning, NumPy is frequently used to handle matrix operations, numerical optimizations, and data preprocessing, all of which are crucial for training agents to learn from environments provided by OpenAI Gym. By leveraging NumPy’s computational efficiency, developers can streamline the creation of complex models and speed up the training process.

Pandas complements NumPy by offering powerful data structures like Series and DataFrames, which are tailored for data manipulation and analysis. In reinforcement learning scenarios, Pandas simplifies the handling of large datasets, allowing developers to clean, organize, and visualize data with ease. Whether it’s logging agent performance, analyzing simulation results, or preparing datasets for training, Pandas provides an intuitive interface to manage data pipelines. It also integrates seamlessly with NumPy, enabling smooth transitions between raw numerical data and structured analysis.

When combined, OpenAI Gym, NumPy, and Pandas** create a robust foundation for reinforcement learning research and development. OpenAI Gym provides the environments to test agents, NumPy ensures efficient numerical computation, and Pandas facilitates data management and analysis. Together, they empower developers to iterate quickly, run experiments, and fine-tune their models, driving innovation in reinforcement learning.

To subscribe to this product from Azure Marketplace and initiate an instance using the Azure compute service, follow these steps:

1. Navigate to Azure Marketplace and subscribe to the desired product.
2. Search for “virtual machines” and select “Virtual machines” under Services.
3. Click on “Add” in the Virtual machines page, which will lead you to the Create a virtual machine page.
4. In the Basics tab:

• • Ensure the correct subscription is chosen under Project details.
  • Opt for creating a new resource group by selecting “Create new resource group” and name it as “myResourceGroup.”

5. Under Instance details:

• • Enter “myVM” as the Virtual machine name.
  • Choose “East US” as the Region.
  • Select “Ubuntu 18.04 LTS” as the Image.
  • Leave other settings as default.

6. For Administrator account:

• • Pick “SSH public key.”
  • Provide your user name and paste your public key, ensuring no leading or trailing white spaces.

7. Under Inbound port rules > Public inbound ports:

• • Choose “Allow selected ports.”
  • Select “SSH (22)” and “HTTP (80)” from the drop-down.

8. Keep the remaining settings at their defaults and click on “Review + create” at the bottom of the page.
9. The “Create a virtual machine” page will display the details of the VM you’re about to create. Once ready, click on “Create.”
10. The deployment process will take a few minutes. Once it’s finished, proceed to the next section.

To connect to the virtual machine:

1. Access the overview page of your VM and click on “Connect.”
2. On the “Connect to virtual machine” page:

• • Keep the default options for connecting via IP address over port 22.
  • A connection command for logging in will be displayed. Click the button to copy the command. Here’s an example of what the SSH connection command looks like:
    “`
    ssh [email protected]
    “`

3. Using the same bash shell that you used to generate your SSH key pair, you can either reopen the Cloud Shell by selecting >_ again

or going to https://shell.azure.com/bash.
4. Paste the SSH connection command into the shell to initiate an SSH session.

Usage/Deployment Instructions

         Anarion Technologies-OpenAI-Gym with Numpy & Pandas

Note: Search product on Azure marketplace and click on “Get it now”

Click on Continue

Click on Create

Creating a Virtual Machine, enter or select appropriate values for zone, machine type, resource group and so on as per your choice.