The Evolved Policy Gradients (EPG) is a fascinating implementation in the realm of artificial intelligence, designed to enhance policy gradient methods in reinforcement learning. This guide will walk you through the installation, running, testing, and visualization of EPG, ensuring you can explore this innovation with ease.

Installation

To get started with EPG, you’ll first need to install several dependencies. Here’s a step-by-step breakdown:

• Install Anaconda:
  • Download the Miniconda installation script:

  curl -o tmpminiconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh

  • Run the installation script:

  bash tmpminiconda.sh

• Create a new conda environment for EPG:

  conda create -n epg python=3.6.1

  source activate epg

• Install necessary macOS packages for MPI:

  brew install open-mpi

• Install necessary Python packages:

  pip install mpi4py==3.0.0 scipy pandas tqdm joblib cloudpickle==0.5.2 progressbar2 opencv-python flask==0.11.1 matplotlib pytest cython chainer pathos mujoco_py gym[all]

Running the EPG Code

After installation, you can run the EPG code with the following steps:

1. Change directory to the EPG code folder:

   cd path_to_EPG_folder

2. Launch the entry script:

   PYTHONPATH=. python epglaunch_local.py

3. Your experiment data will be saved in:

   home_dir/EPG_experiments/month-day/experiment_name

Testing Your Setup

To ensure everything is working correctly, follow these steps:

1. Set the path for the `theta_load_path` in `launch_local.py` to the correct location of `theta.npy`, which should be in:

   home_dir/EPG_experiments/month-day/experiment_name/thetas

2. Run the test command:

   PYTHONPATH=. python epglaunch_local.py --test true

Visualizing Experiment Data

After running experiments, you might want to visualize the data. Here’s how you can do that:

1. Start the visualization script by specifying the path:

   PYTHONPATH=. python epgviskitfrontend.py home_dir/EPG_experiments/month-day/experiment_name

2. Open your web browser and navigate to:
   http://0.0.0.0:5000 to see your experiment visualizations.

Troubleshooting

If you encounter any issues during installation or execution, consider the following troubleshooting tips:

• Ensure all dependencies are installed correctly. Missing packages can lead to errors.
• Check if your Python version matches the specified version (3.6.1) as some libraries may not be compatible with newer versions.
• If running into permissions issues, try running your terminal as an administrator.
• For unexpected errors, looking at the README or project-specific forums might offer insights.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following this guide, you are well on your way to exploring the capabilities of Evolved Policy Gradients. Remember, during the setup process, thinking of dependencies as ingredients in a recipe can help you avoid missing items and mistakes. Just like baking, ensure you have everything prepared before putting it all together!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

References

Houthooft, R., Chen, R. Y., Isola, P., Stadie, B. C., Wolski, F., Ho, J., Abbeel, P. (2018). Evolved Policy Gradients. arXiv preprint arXiv:1802.04821.

For a demonstration, check out the video at: Evolved Policy Gradients Demonstration.