The Balloon Learning Environment - flying stratospheric balloons with deep reinforcement learning.

Last update: Dec 25, 2022

Overview

Balloon Learning Environment

The Balloon Learning Environment (BLE) is a simulator for stratospheric balloons. It is designed as a benchmark environment for deep reinforcement learning algorithms, and is a followup to the Nature paper "Autonomous navigation of stratospheric balloons using reinforcement learning".

Getting Started

Note: The BLE requires python >= 3.7

The BLE can easily be installed with pip:

pip install --upgrade pip && pip install balloon_learning_environment

Once the package has been installed, you can test it runs correctly by evaluating one of the benchmark agents:

python -m balloon_learning_environment.eval.eval \
  --agent=station_seeker \
  --renderer=matplotlib \
  --suite=micro_eval \
  --output_dir=/tmp/ble/eval

Ensure the BLE is Using Your GPU/TPU

The BLE contains a VAE for generating winds, which you will probably want to run on your accelerator. See the jax documentation for installing with GPU or TPU.

As a sanity check, you can open interactive python and run:

from balloon_learning_environment.env import balloon_env
env = balloon_env.BalloonEnv()

If you are not running with GPU/TPU, you should see a log like:

WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)

If you don't see this log, you should be good to go!

Next Steps

For more information, see the docs.

Giving credit

If you use the Balloon Learning Environment in your work, we ask that you use the following BibTeX entry:

@software{Greaves_Balloon_Learning_Environment_2021,
  author = {Greaves, Joshua and Candido, Salvatore and Dumoulin, Vincent and Goroshin, Ross and Ponda, Sameera S. and Bellemare, Marc G. and Castro, Pablo Samuel},
  month = {12},
  title = {{Balloon Learning Environment}},
  url = {https://github.com/google/balloon-learning-environment},
  version = {1.0.0},
  year = {2021}
}

If you use the ble_wind_field dataset, you should also cite

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A.,
Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G.,
Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M.,
Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L.,
Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P.,
Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F.,
Villaume, S., Thépaut, J-N. (2017): Complete ERA5: Fifth generation of ECMWF
atmospheric reanalyses of the global climate. Copernicus Climate Change Service
(C3S) Data Store (CDS). (Accessed on 01-04-2021)

The Balloon Learning Environment - flying stratospheric balloons with deep reinforcement learning.

Related tags

Overview

Balloon Learning Environment

Getting Started

Ensure the BLE is Using Your GPU/TPU

Next Steps

Giving credit

Owner

Google

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The Balloon Learning Environment - flying stratospheric balloons with deep reinforcement learning.

Related tags

Overview

Balloon Learning Environment

Getting Started

Ensure the BLE is Using Your GPU/TPU

Next Steps

Giving credit

Owner

Google

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Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021

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