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balloon-learning-environment

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

https://github.com/google/balloon-learning-environment

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: nature.com
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.8%) to scientific vocabulary

Keywords

deep-learning machine-learning reinforcement-learning
Last synced: 9 months ago · JSON representation ·

Repository

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

Basic Info
  • Host: GitHub
  • Owner: google
  • License: apache-2.0
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 56.3 MB
Statistics
  • Stars: 124
  • Watchers: 7
  • Forks: 15
  • Open Issues: 11
  • Releases: 0
Topics
deep-learning machine-learning reinforcement-learning
Created over 4 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Contributing License Citation

README.md

Balloon Learning Environment

Docs



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

To install with the acme package:

$ pip install --upgrade pip $ pip install balloon_learning_environment[acme]

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

To install from GitHub directly, run the following commands from the root directory where you cloned the repository:

$ pip install --upgrade pip $ pip install .[acme]

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)

Owner

  • Name: Google
  • Login: google
  • Kind: organization
  • Email: opensource@google.com
  • Location: United States of America

Google ❤️ Open Source

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Greaves"
  given-names: "Joshua"
- family-names: "Candido"
  given-names: "Salvatore"
- family-names: "Dumoulin"
  given-names: "Vincent"
- family-names: "Goroshin"
  given-names: "Ross"
- family-names: "Ponda"
  given-names: "Sameera S."
- family-names: "Bellemare"
  given-names: "Marc G."
- family-names: "Castro"
  given-names: "Pablo Samuel"
title: "Balloon Learning Environment"
version: 1.0.0
date-released: 2021-12-06
url: "https://github.com/google/balloon-learning-environment"

GitHub Events

Total
  • Watch event: 7
  • Issue comment event: 2
  • Pull request event: 4
  • Fork event: 1
Last Year
  • Watch event: 7
  • Issue comment event: 2
  • Pull request event: 4
  • Fork event: 1

Committers

Last synced: 10 months ago

All Time
  • Total Commits: 139
  • Total Committers: 11
  • Avg Commits per committer: 12.636
  • Development Distribution Score (DDS): 0.532
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Joshua Greaves j****s@g****m 65
Pablo Samuel Castro p****c@g****m 34
Balloon Learning Environment Team n****y@g****m 10
Vincent Dumoulin v****n@g****m 9
Piotr Stanczyk s****k@g****m 6
Jesse Farebrother j****o@g****m 4
Rishabh Agarwal r****l@g****m 3
Danila Sinopalnikov s****v@g****m 3
Bobak Shahriari b****r@g****m 3
Rebecca Chen r****n@g****m 1
Max Schwarzer m****r@g****m 1
Committer Domains (Top 20 + Academic)
google.com: 11

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 10
  • Total pull requests: 30
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 2 months
  • Total issue authors: 7
  • Total pull request authors: 4
  • Average comments per issue: 1.1
  • Average comments per pull request: 0.8
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 26
Past Year
  • Issues: 0
  • Pull requests: 3
  • Average time to close issues: N/A
  • Average time to close pull requests: 2 minutes
  • Issue authors: 0
  • Pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 1.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • 1jskk (3)
  • SaundersJE97 (2)
  • joshcarterhi (1)
  • 1kaiser (1)
  • 2462954048 (1)
  • tkschuler (1)
  • juno53211 (1)
Pull Request Authors
  • dependabot[bot] (26)
  • jiawei-janna-lin (2)
  • hslyu (1)
  • selym3 (1)
Top Labels
Issue Labels
Pull Request Labels
dependencies (26)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 107 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 10
  • Total maintainers: 1
pypi.org: balloon-learning-environment
  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 107 Last month
Rankings
Dependent packages count: 10.1%
Downloads: 18.9%
Average: 32.1%
Dependent repos count: 67.3%
Maintainers (1)
joshgreaves
Last synced: 9 months ago

Dependencies

docs/requirements.txt pypi
  • absl-py ==1.0.0
  • dopamine-rl ==4.0.1
  • flax ==0.3.6
  • gin-config ==0.5.0
  • gym ==0.21.0
  • jax ==0.2.25
  • jaxlib ==0.1.74
  • myst-parser ==0.15.2
  • numpy ==1.21.4
  • opensimplex ==0.3
  • s2sphere ==0.2.5
  • sklearn ==0.0
  • tensorflow ==2.7.0
  • tensorflow-probability ==0.15.0
  • transitions ==0.8.10
requirements.txt pypi
  • Keras-Preprocessing ==1.1.2
  • Markdown ==3.3.4
  • Pillow ==8.4.0
  • Werkzeug ==2.0.2
  • absl-py ==0.15.0
  • ale-py ==0.7.3
  • astunparse ==1.6.3
  • cached-property ==1.5.2
  • cachetools ==4.2.4
  • certifi ==2021.10.8
  • charset-normalizer ==2.0.7
  • chex ==0.0.8
  • clang ==5.0
  • cloudpickle ==2.0.0
  • cycler ==0.11.0
  • decorator ==5.1.0
  • dm-tree ==0.1.6
  • dopamine-rl ==4.0.0
  • flatbuffers ==1.12
  • flax ==0.3.6
  • future ==0.18.2
  • gast ==0.4.0
  • gin ==0.1.6
  • gin-config ==0.5.0
  • google-auth ==1.35.0
  • google-auth-oauthlib ==0.4.6
  • google-pasta ==0.2.0
  • grpcio ==1.41.1
  • gym ==0.21.0
  • h5py ==3.1.0
  • idna ==3.3
  • importlib-metadata ==4.8.1
  • importlib-resources ==5.4.0
  • jax ==0.3.0
  • jaxlib ==0.3.0
  • joblib ==1.1.0
  • keras ==2.7.0
  • kiwisolver ==1.3.2
  • libclang ==12.0.0
  • matplotlib ==3.4.3
  • msgpack ==1.0.2
  • numpy ==1.19.5
  • oauthlib ==3.1.1
  • opencv-python ==4.5.4.58
  • opensimplex ==0.3
  • opt-einsum ==3.3.0
  • optax ==0.0.9
  • pandas ==1.3.4
  • protobuf ==3.19.1
  • pyasn1 ==0.4.8
  • pyasn1-modules ==0.2.8
  • pygame ==2.0.3
  • pyparsing ==3.0.4
  • python-dateutil ==2.8.2
  • pytz ==2021.3
  • requests ==2.26.0
  • requests-oauthlib ==1.3.0
  • rsa ==4.7.2
  • s2sphere ==0.2.5
  • scikit-learn ==1.0.1
  • scipy ==1.7.1
  • six ==1.15.0
  • sklearn ==0.0
  • tensorboard ==2.6.0
  • tensorboard-data-server ==0.6.1
  • tensorboard-plugin-wit ==1.8.0
  • tensorflow ==2.7.0rc1
  • tensorflow-estimator ==2.7.0
  • tensorflow-io-gcs-filesystem ==0.21.0
  • tensorflow-probability ==0.16.0
  • termcolor ==1.1.0
  • tf-slim ==1.1.0
  • tfp-nightly ==0.15.0.dev20211104
  • threadpoolctl ==3.0.0
  • toolz ==0.11.1
  • transitions ==0.8.10
  • typing-extensions ==3.7.4.3
  • urllib3 ==1.26.7
  • wrapt ==1.12.1
  • zipp ==3.6.0
pyproject.toml pypi
setup.py pypi