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Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning

This repository is the official implementation of multi-agent task embeddings (MATE)

Dependencies

Clone and install codebase with relevant dependencies using the provided setup.py with

$ git clone <git-url>
$ cd <repo>
$ pip install -e .

We recommend to install dependencies in a virtual environment. Code was tested with Python 3.9.12.

To install the BoulderPush environment, run

$ git clone https://github.com/LukasSchaefer/boulderpush
$ cd boulderpush
$ pip install -e .

For our implementation, we defined new tasks within the multi-robot warehouse and multi-agent particle environments. To use these tasks, we provide modified code of the RWARE repository as well as modified code of PettingZoo which contains our tasks. To install, run

$ https://github.com/LukasSchaefer/pettingzoo
$ cd PettingZoo
$ pip install -e .

and

$ git clone https://github.com/LukasSchaefer/robotic-warehouse
$ cd robotic-warehouse
$ pip install -e .

Training

Within the mate directory execute

$ python3 run.py +env=<ENV> +algorithm=maa2c seed=<SEED> +autoencoder=<MATE> logger=<LOGGER>

to train an algorithm in the specified environment. For logging, we provide a tensorboard and print logger which log into a tensorboard file or print out each logged metric in the console, respectively. For MATE, select

  • cen_mate
  • ind_mate
  • mix_mate for respective MATE paradigms used during training.

To adjust the training duration, model saving frequency, logging frequency and evaluation frequency, adjust the following configuration values (all in timesteps):

  • algorithm.num_env_steps: total number of training timesteps
  • algorithm.log_interval: interval at which logger reports progress in console
  • algorithm.save_interval: interval at which MARL and MATE models are saved
  • algorithm.eval_interval: interval at which evaluation episodes will be executed

Fine-tuning

After training, model parameters are saved in the respective hydra output directory. Provide the additional algorithm.load_dir=<PATH/TO/DIR/WITH/MODELS> to load parameters for the agents and MATE to fine-tune in a new task. Note, the path needs to be provided as a absolute path.

Codebase Structure

Hydra Configurations

The interface of the main run script run.py is handled through Hydra with a hierarchy of configuration files under configs/. These are structured in packages for

  • MARL algorithms under configs/algorithm/ (only MAA2C provided at the current time)
  • environments under configs/env/
  • MATE paradigms under configs/autoencoder_algorithm/
  • hydra parameters under configs/hydra/
  • logger parameters under configs/logger/
  • default parameters in configs/default.yaml

Results and Plots From Paper

Episodic returns of all experiments can be found under data/, containing the configs and results of the respective training and finetuning runs across all algorithms and tasks. To load the data and reproduce all plots in the paper, see the jupyter notebook at data/plot_paper_results.ipynb.

Citation

@inproceedings{schaefer2023mate,
  title={Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning},
  author={Sch{\"a}fer, Lukas and Christianos, Filippos and Storkey, Amos and Albrecht, Stefano V.},
  booktitle={NeurIPS Workshop on Generalization in Planning},
  year={2023}
}