IL-Datasets 0.3.0

A package for creating IL datasets

IL Datasets

Hi, welcome to the Imitation Learning (IL) Datasets. Something that always bothered me a lot was how difficult it was to find good weights for an expert, or trying to create a dataset for different state-of-the-art methods. For this reason I've created this repository in an effort to make it more accessible for researches to create datasets using experts from the Hugging Face.

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How does it work?

This project also works with multithreading, which should accelerate the dataset creation. It consists of one Controller class, which requires two different functions to work: (i) a enjoy function (for the agent to play and record an episode); and a (ii) collate function (for putting all episodes together).

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The enjoy function will receive 3 parameters and return 1:

• path: str - where the episode is going to be recorded

• experiment: Context - A class for recording all information (if you don't want to use print - keeping the console clear)

• expert: Policy - A model based on the StableBaselines3 BaseAlgorithm.

• returns: bool - Whether it was successfull or not

Obs: To use the model you can call predict, the policy class already has the correct form of using it (a.k.a., how the StableBaselines3 uses).

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The collate function will receive 2 parameters and return 1:

• path: str - where it should save the final dataset

• episodes: list[str] - A list of paths for each file

• returns: bool - Whether it was successfull or not

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Requirements

I did use Python=3.9 during development.
All other requirements are listed in requirements.txt.

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Registering new experts

If you would like to add new experts locally, you can call the Experts class. It uses the following structure:

• identifier: str - A name for calling the expert.
• policy: Policy - A dataclass with:
  • name: str - Gym Environment name
  • repo_id: str - Hugging Face repo indentification
  • filename: str - Weights file name
  • threshold: float - How much reward should the episode accumulate to be considered good
  • algo: BaseAlgorithm - The class from StableBaselines3

Obs: If not using StableBaselines, the expert has to have a predict function that receives:

• obs: Tensor - Current environment state
• state: Tensor - Model's internal state
• deterministic: bool - If it should explore or not

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This repository is in development

Here is a list of the upcoming releases:

• Collate function support
• Support for installing as a dependency
• Module for downloading trajectories from a Hugging Face dataset
  • Create benchmark for data
• Create actual documentation
• Create some examples
• Create tests
• Create GitHub workflow with pylint

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If you like this repository be sure to check my other projects:

Development

• An easy to use Wrapper for Tensorboard
• A watcher for python to facilitate development of small projects

Academic

• Imitating Unknown Policies via Exploration (BMVC)
• Augmented behavioral cloning from observation (IJCNN)
• Self-supervised imitation learning from observation (MSc dissertation)