This repository is the official implementation of Shared Experience Actor Critic.
For the experiments in LBF and RWARE, please install from:
Also requires, PyTorch 1.6+
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=25or any other foraging environment size/configuration.env_name=rware-tiny-2ag-v1 time_limit=500env_name=rware-tiny-4ag-v1 time_limit=500- ...
env_name=rware-tiny-2ag-hard-v1 time_limit=500or any other rware environment size/configuration.
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-v0or 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.
To load and render the pretrained models in SEAC, run in the seac directory
python evaluate.py
@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}
}