Discovering and Achieving Goals via World Models
[Project Website] [Benchmark Code] [Video (2min)] [Oral Talk (13min)] [Paper]
Russell Mendonca*1, Oleh Rybkin*2, Kostas Daniilidis2, Danijar Hafner3,4, Deepak Pathak1
(* equal contribution, random order)
1Carnegie Mellon University
2University of Pennsylvania
3Google Research, Brain Team
4University of Toronto
Official implementation of the Lexa agent from the paper Discovering and Achieving Goals via World Models.
Setup
Create the conda environment by running :
conda env create -f environment.yml
Clone the lexa-benchmark repo, and modify the python path
export PYTHONPATH=
Export the following variables for rendering
export MUJOCO_RENDERER=egl; export MUJOCO_GL=egl
Training
First source the environment : source activate lexa
For training, run :
export CUDA_VISIBLE_DEVICES=
python train.py --configs defaults
--task
--logdir
where method can be lexa_temporal
, lexa_cosine
, ddl
, diayn
or gcsl
Supported tasks are dmc_walker_walk
, dmc_quadruped_run
, robobin
, kitchen
, joint
To view the graphs and gifs during training, run tensorboard --logdir
Bibtex
If you find this code useful, please cite:
@misc{lexa2021,
title={Discovering and Achieving Goals via World Models},
author={Mendonca, Russell and Rybkin, Oleh and
Daniilidis, Kostas and Hafner, Danijar and Pathak, Deepak},
year={2021},
Booktitle={NeurIPS}
}
Acknowledgements
This code was developed using Dreamer V2 and Plan2Explore.