seac's Introduction

Shared Experience Actor Critic

This repository is the official implementation of Shared Experience Actor Critic.

Requirements

For the experiments in LBF and RWARE, please install from:

Also requires, PyTorch 1.6+

Training - SEAC

To train the agents in the paper, navigate to the seac directory:

cd seac

And run:

python train.py with <env config>

Valid environment configs are:

env_name=Foraging-15x15-3p-4f-v0 time_limit=25
...
env_name=Foraging-12x12-2p-1f-v0 time_limit=25 or any other foraging environment size/configuration.
env_name=rware-tiny-2ag-v1 time_limit=500
env_name=rware-tiny-4ag-v1 time_limit=500
...
env_name=rware-tiny-2ag-hard-v1 time_limit=500 or any other rware environment size/configuration.

Training - SEQL

To train the agents in the paper, navigate to the seac directory:

cd seql

And run the training script. Possible options are:

python lbf_train.py --env Foraging-12x12-2p-1f-v0
...
python lbf_train.py --env Foraging-15x15-3p-4f-v0 or any other foraging environment size/configuration.
python rware_train.py --env "rware-tiny-2ag-v1"
...
python rware_train.py --env "rware-tiny-4ag-v1"or any other rware environment size/configuration.

Evaluation/Visualization - SEAC

To load and render the pretrained models in SEAC, run in the seac directory

python evaluate.py

Citation

@inproceedings{christianos2020shared,
  title={Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning},
  author={Christianos, Filippos and Sch{\"a}fer, Lukas and Albrecht, Stefano V},
  booktitle = {Advances in Neural Information Processing Systems},
  year={2020}
}

Recommend Projects