2.1 - Installing RL Libraries

Key Changes: This revision elevates the documentation from a simple list of commands to a professional setup guide. It introduces a "Project Dependencies" table to clearly define the role of each library, providing essential context for a developer. The installation process is framed as a formal "Setup Workflow" checklist, and a crucial note about PyTorch as the deep learning backend has been added. The verification step is also made more robust by including the library versions, which is a standard practice for ensuring a reproducible environment.

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2.1 - Installing RL Libraries

To build and train a learning agent, our project requires several key libraries from the Python scientific computing ecosystem. This section outlines the dependencies and provides a step-by-step workflow for setting up a clean, isolated development environment.

Project Dependencies

Our implementation will rely on the following core packages:

Library	Role
gymnasium	The modern standard for defining Reinforcement Learning environments. It provides the API contract our SC2GymEnv wrapper will adhere to.
stable-baselines3	A high-performance library of pre-implemented RL algorithms (like PPO). This will serve as the "brain" or Agent.
torch	The deep learning framework used by stable-baselines3 to define and train the neural network models.
tensorboard	A visualization toolkit for inspecting the training process, allowing us to graph rewards and other metrics.

Setup Workflow

The installation is a three-step process designed to ensure a stable and reproducible environment.

• Step 1: Create and activate a dedicated virtual environment.
• Step 2: Install the required packages via pip.
• Step 3: Verify the installation and library versions.

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Step 1 - Create and Activate a Virtual Environment

This is a critical best practice to prevent dependency conflicts.

1. Navigate to your project folder in your terminal.
2. Create the environment (e.g., named venv):

   python -m venv venv

3. Activate it:
   • Windows (Command Prompt / PowerShell):

     .\venv\Scripts\activate

   • macOS / Linux:

     source venv/bin/activate

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Step 2 - Install Libraries via pip

With the virtual environment active, a single command will install all necessary packages.

pip install "stable-baselines3[extra]>=2.0.0"

• Why this command?
  • stable-baselines3: Installs the core library.
  • [extra]: This is a crucial addition. It automatically pulls in gymnasium, torch, and tensorboard, satisfying all our dependencies in one step.
  • >=2.0.0: Specifies a minimum version to ensure API compatibility.

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Step 3 - Verify Installation

Confirm that all packages were installed correctly and check their versions.

1. In your active terminal, start a Python interpreter by typing python.

2. At the Python prompt (>>>), run the following code:

   try:
       import gymnasium
       import stable_baselines3
       import torch
   
       print("--- Verification Successful ---")
       print(f"gymnasium version: {gymnasium.__version__}")
       print(f"stable-baselines3 version: {stable_baselines3.__version__}")
       print(f"torch version: {torch.__version__}")
       print("-----------------------------")
   
   except ImportError as e:
       print(f"An error occurred: {e}")
       print("Please check your installation.")
   
   ```If you see the version numbers without any errors, your environment is correctly configured, and you are ready to implement the `SC2GymEnv` wrapper.