How to Get Started with ProMP: Proximal Meta-Policy Search

Welcome to your go-to guide for the ProMP repository! This article will walk you through the essentials of getting started with Proximal Meta-Policy Search (ProMP). Designed for those who may feel daunted by the complexities of Meta Reinforcement Learning (Meta-RL), this guide aims to simplify the process and make it user-friendly.

What is ProMP?

ProMP, short for Proximal Meta-Policy Search, is a sophisticated algorithm designed to optimize reinforcement learning tasks by utilizing past experience for better decision-making on new tasks. The repository contains two branches to suit your needs:

• master: A lightweight branch that allows you to run Meta-RL algorithms seamlessly.
• full-code: A comprehensive branch that includes experimental scripts and extensive code for replicating results from the paper by Rothfuss et al. (2018).

Installation Guide

Before diving into the ProMP code, proper setup is vital. You have two options for installation:

A. Using Docker

• First, ensure Docker is installed on your machine. You can set up Docker by following these instructions.
• Then, pull the Docker container with the command: docker pull jonasrothfusspromp
• All necessary dependencies are pre-installed within the Docker container.

B. Installing Dependencies Locally

B.1 Installing MPI

• Make sure you have a working MPI implementation. For Ubuntu users, you can run: sudo apt-get install libopenmpi-dev

B.2 Create and Activate a Python Environment

You can use either Virtualenv or Anaconda.

• Virtualenv:
  1. Install Virtualenv: pip install --upgrade virtualenv
  2. Create a virtual environment: virtualenv venv-name
  3. Activate it: source venv-name/bin/activate
• Anaconda:
  1. If you haven’t yet, install Anaconda.
  2. Create and activate an environment: conda create -n env-name python=3.6 and source activate env-name

B.3 Install Python Dependencies

Run: pip install -r requirements.txt

B.4 Set Up Mujoco

Most environments require the Mujoco physics engine. Follow the setup instructions for Mujoco and mujoco-py.

Running ProMP

Once you’ve set everything up, running the ProMP algorithm is straightforward.

Without Mujoco

For a point environment, execute:

python run_scripts/pro-mp_run_point_mass.py

With Mujoco

To run in a Mujoco environment, execute:

python run_scripts/pro-mp_run_mujoco.py

The run configuration can be modified directly in the script or by specifying a JSON configuration file with required hyperparameters.

python run_scripts/pro-mp_run.py --config_file config_file_path --dump_path dump_path

Troubleshooting Tips

If you run into issues during installation or execution, consider the following:

• Recheck your installation of Docker or Python packages to ensure all dependencies are met.
• Ensure that the Mujoco physics engine is correctly set up for the environments that require it.
• If encountering environment issues, try to refresh your virtual environment or Docker container.

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Conclusion

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.