Welcome to the world of optimal control! A place where theoretical frameworks merge with practical applications, especially in complex industrial scenarios. This blog will guide you through the installation and usage of the General Optimal control Problem Solver (GOPS). Buckle up; let’s dive in!

What is GOPS?

GOPS serves as a robust toolset to tackle optimal control problems, especially suitable for high-dimensional, nonlinear, and random challenges. Developed by the Intelligent Driving Laboratory (iDLab), this tool utilizes Reinforcement Learning and Approximate Dynamic Programming to enhance real-time performance in industrial control applications.

Installation Steps

Follow these steps to install GOPS on your system:

1. System Requirements:

   Ensure your system meets the following requirements:

   • Windows 7 or greater or Linux
   • Python 3.6 or greater (Python 3.8 is recommended)
   • (Optional) Matlab/Simulink 2018a or greater
   • The installation path must consist of English characters only
2. Clone the Repository:

   git clone https://github.com/Intelligent-Driving-Laboratory/GOPS.git
   cd GOPS

3. Create a Conda Environment:

   Depending on your OS, run one of the following commands:

   # for Linux
   conda env create -f gops_environment.nix.yml
   conda activate gopsor
   
   # for Windows
   conda env create -f gops_environment.win.yml
   conda activate gops

4. Install GOPS:

   pip install -e .

5. (Optional) Install cyipopt:

   If you plan to utilize the MPC-based optimal controller:

   conda install -c conda-forge cyipopt

Quick Start Guide

Now that you have GOPS installed, let’s kickstart your journey! Here’s how to run the finite-horizon Approximate Dynamic Programming (FHADP) in the inverted double pendulum environment:

1. Train the policy by executing the following command:

python example_trainfhadp/fhadp_mlp_idpendulum_serial.py

2. After training, test the policy:

python example_run/run_idp_fhadp.py

3. To record a video of your running policy, set save_render=True in your testing script.

Understanding the Code: Analogy

Think of using the GOPS tool as training a puppy. In the beginning, you need to set up a comforting environment (installation). Just like with your furry friend, you have to establish the right surroundings before you can train them (create the conda environment).

Next, you have to teach your puppy commands (training the policy). With practice and reinforcement, your puppy learns to follow your lead, and similarly, with the correct inputs, the model learns and improves over time. Finally, once trained, you’ll want to show off your well-behaved puppy to your friends (testing the policy).

Troubleshooting

If you encounter problems during installation or while executing commands, here are a few troubleshooting tips to keep in mind:

• Ensure that all system requirements are met.
• Check your internet connection while cloning the repository and installing packages.
• Make sure your Python version is compatible with the GOPS requirements.
• If you run into issues with packages, consider updating Conda or Pip.
• Consult the documentation at gops.readthedocs.io for detailed instructions.

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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.