Welcome to the world of Lagom! In this blog, we’re going to explore how to utilize Lagom, which is designed to help you prototype reinforcement learning (RL) algorithms swiftly and efficiently.

Why Choose Lagom?

Lagom embodies a philosophical approach inspired by its Swedish namesake, meaning “not too much and not too little, simplicity is often the best.” This library strikes a balance between flexibility and usability, running on top of PyTorch while providing modular tools for quick prototyping. With a careful design, it allows you to avoid the pitfalls of both overly complex libraries and those that lack the necessary depth for experimentation.

Installation

Before jumping into using Lagom, you need to set it up on your machine. Here’s how you can do that:

• Create a Miniconda Environment:

conda create -n lagom python=3.7

• Install Dependencies:

pip install -r requirements.txt

• Install Lagom from Source:

git clone https://github.com/zuoxingdong/lagom.git

cd lagom

pip install -e .

Installing from the source is useful for research purposes as you can modify and adapt the code as needed. Additionally, we recommend checking out the bash scripts available in the scripts directory for automatic setups.

How to Use Lagom

Using Lagom can be distilled into a simple pipeline. Think of each step as a stage in a relay race, where each runner plays a critical role in ensuring the team’s success:

1. Define your RL agent: This is your runner representing the algorithm.
2. Define your environment: Think of this as the track where your agent races.
3. Define your engine: This is the coach guiding how the agent trains and evaluates.
4. Define your configurations: Set up the strategy used for hyperparameter searches.
5. Run the experiment pipeline: This is where your agent hits the track!
6. Parallelize your experiments: Just like multiple agents successfully completing the race at the same time!

Troubleshooting Ideas

If you encounter issues during installation or usage, consider the following troubleshooting steps:

• Ensure that you have the correct version of Python and that all dependencies are correctly installed.
• Check for error messages in the terminal during installation; they can provide clues to what went wrong.
• If hyperparameter tuning isn’t yielding results, revisit your configurations to ensure they are aligned with your goals.
• For issues with parallelization, verify your system’s resources to ensure they are sufficient.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Documentation and Examples

Explore additional resources in the Lagom Documentation, or refer to simple examples provided in the examples directory to see how everything works in practice.

What’s New?

Lagom regularly updates its features to improve user experience:

• 2019-03-04 (v0.0.3): Much easier and cleaner APIs
• 2018-11-04 (v0.0.2): Enhanced high-level API designs; increased unit tests
• 2018-09-20 (v0.0.1): Initial release

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.