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# BIG-Gym | ||
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BIG-Gym is a *crowd-sourcing* challenge for RL *environments* and *behaviors*, inspired by [BIG-Bench](https://github.com/google/BIG-bench). It is co-organized as part of NeurIPS 2021 [Ecology Theory for RL (EcoRL)](https://sites.google.com/view/ecorl2021/home) workshop. *Our goal is to create the "ImageNet" for continuous control, with diversity in agent morphologies, environment scenes, objects, and tasks.* We solicit submissions for two tracks: **Open-Ended Creativity Track** and **Goal-Oriented Competition Track**. | ||
BIG-Gym is a *crowd-sourcing* challenge for RL *environments* and *behaviors*, inspired by [BIG-Bench](https://github.com/google/BIG-bench). *Our goal is to create the "ImageNet" for continuous control, with diversity in agent morphologies, environment scenes, objects, and tasks.* We solicit submissions for two tracks: **Open-Ended Creativity Track** and **Goal-Oriented Competition Track**. | ||
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```python | ||
from brax.experimental import biggym | ||
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## Organizers | ||
# register all in registry/__init__.py | ||
biggym.register_all(verbose=True) | ||
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# register a specific folder under registry/ | ||
# `biggym.ENVS_BY_TRACKS` shows which envs are registered under each track | ||
env_names, component_names, task_env_names = biggym.register(registry_name) | ||
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# (optional) inspect and get default configurable parameters of an environment | ||
env_params, _ = biggym.inspect_env(env_names[0]) | ||
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# create an environment | ||
env = biggym.create(env_names[0], env_params=env_params) | ||
``` | ||
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Core organizers: [Shixiang Shane Gu](https://sites.google.com/view/gugurus/home), [Hiroki Furuta](https://frt03.github.io/), [Manfred Diaz](https://manfreddiaz.github.io/) | ||
Challenge details (timelines, submission instructions) are [here](https://sites.google.com/view/rlbiggym). | ||
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Supported by: | ||
## Citing | ||
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If you use BIG-Gym in a publication, please cite referenced libraries: | ||
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``` | ||
@article{gu2021braxlines, | ||
title={Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization}, | ||
author={Gu, Shixiang Shane and Diaz, Manfred and Freeman, Daniel C and Furuta, Hiroki and Ghasemipour, Seyed Kamyar Seyed and Raichuk, Anton and David, Byron and Frey, Erik and Coumans, Erwin and Bachem, Olivier}, | ||
journal={arXiv preprint arXiv:2110.04686}, | ||
year={2021} | ||
} | ||
@software{brax2021github, | ||
author = {C. Daniel Freeman and Erik Frey and Anton Raichuk and Sertan Girgin and Igor Mordatch and Olivier Bachem}, | ||
title = {Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation}, | ||
url = {http://github.com/google/brax}, | ||
version = {0.0.5}, | ||
year = {2021}, | ||
} | ||
``` | ||
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## Organizers | ||
* [Shixiang Shane Gu](https://sites.google.com/view/gugurus/home) (Google Brain), [Hiroki Furuta](https://frt03.github.io/) (University of Tokyo), [Manfred Diaz](https://manfreddiaz.github.io/) (University of Montreal) | ||
* [Brax](https://github.com/google/brax)/[Braxlines](https://arxiv.org/abs/2110.04686) teams | ||
* [NeurIPS 2021 EcoRL workshop](https://sites.google.com/view/ecorl2021/home) organizers |
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# Copyright 2021 The Brax Authors. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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"""Registry of BIG-Gym environments and components.""" | ||
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# keep alphabetical ordering | ||
REGISTRIES = [ | ||
'jump', | ||
'proant', | ||
] | ||
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def get_comp_name(registry_name: str, comp_name: str): | ||
return f'{registry_name}__{comp_name}' | ||
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def get_env_name(registry_name: str, env_name: str): | ||
return f'{registry_name}__{env_name}' |
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brax/experimental/biggym/registry/proant/components/ant.py
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# Copyright 2021 The Brax Authors. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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"""Procedural ant.""" | ||
# pylint:disable=unused-import | ||
from brax.experimental.composer.components.pro_ant import get_specs |
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