This notebook can be used to render Gymnasium (current maintained fork of OpenAI’s Gym) in Google's Colaboratory.
The main approach is to set up a virtual display using the pyvirtualdisplay library. This enables you to render gym environments in Colab, which doesn't have a real display.
Screen.Recording.2023-03-27.at.11.05.58.mov
The advantages of training RL algorithms in colab include:
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Free GPU and TPU access: Google Colab offers free access to GPUs (e.g., NVIDIA Tesla K80, P4, or T4) and TPUs, which can significantly accelerate the training process for RL models that rely on deep learning.
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Pre-installed libraries: Google Colab comes with many popular Python libraries pre-installed, such as TensorFlow, PyTorch, and OpenAI Gym. This can save you time setting up and configuring the necessary tools.
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Collaboration: Colab enables easy collaboration with other researchers and developers. You can share your work, collaborate in real-time, and even leave comments on specific code cells.
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Notebook interface: Colab is built on Jupyter Notebook, which provides an interactive, user-friendly environment for developing, documenting, and visualizing your RL algorithms and results.
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Cloud-based: Since Colab is cloud-based, you can access and run your RL models from any device with internet access. It also allows you to leverage Google Drive for storage and data management.
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Integration with other Google services: Colab can easily integrate with other Google services, such as Google Drive, Sheets, and BigQuery, simplifying data import, export, and analysis.
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Regular updates and maintenance: Google maintains and updates the Colab environment, ensuring that you have access to the latest features and libraries.
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Large user community: Colab has an extensive user community, making it easier to find solutions to common issues, share ideas, and learn from others' experiences.