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https://github.com/Scitator/animal-olympics-starter-kit
https://github.com/Scitator/animal-olympics-starter-kit
Last synced: 8 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/Scitator/animal-olympics-starter-kit
- Owner: Scitator
- License: mit
- Created: 2019-08-08T16:18:04.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-08-11T06:15:10.000Z (over 5 years ago)
- Last Synced: 2024-10-29T14:39:00.040Z (15 days ago)
- Language: Python
- Size: 9.77 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# [NeurIPS 2019: Animal-AI Olympics](http://animalaiolympics.com)
[Catalyst](https://github.com/catalyst-team/catalyst) starter kit
## Environment setup.
(Taken from the official [repo](https://github.com/beyretb/AnimalAI-Olympics)).
The Animal-AI package works on Linux, Mac and Windows, as well as most Cloud providers.
Note that for submission to the competition we only support linux-based Docker files.
We recommend using a virtual environment specifically for the competition. You will need `python3.6` installed (we currently only support **python3.6**).
The main package is an API for interfacing with the Unity environment.
It contains both a [gym environment](https://github.com/openai/gym) as well as an extension of Unity's
[ml-agents environments](https://github.com/Unity-Technologies/ml-agents/tree/master/ml-agents-envs).
You can install it via pip:
```
pip install animalai
```
Or you can install it from the source, head to `animalai/` folder and run `pip install -e .`
Additionally download the environment for your system:
| OS | Environment link |
| --- | --- |
| Linux | [download v1.0.0](https://www.doc.ic.ac.uk/~bb1010/animalAI/env_linux_v1.0.0.zip) |
| MacOS | [download v1.0.0](https://www.doc.ic.ac.uk/~bb1010/animalAI/env_mac_v1.0.0.zip) |
| Windows | [download v1.0.0](https://www.doc.ic.ac.uk/~bb1010/animalAI/env_windows_v1.0.0.zip) |
You can now unzip the content of the archive to the `assets` folder and you're ready to go! Make sure the executable
`AnimalAI.*` is in `assets/`. On linux you may have to make the file executable by running `chmod +x assets/AnimalAI.x86_64`.
Head over to [Quick Start Guide](documentation/quickstart.md) for a quick overview of how the environment works.
tl;dr
```bash
# system requirements
sudo apt-get install xvfb redis-server
# python requirements
conda create -n animal python=3.6 anaconda
source activate animal
pip install -r ./requirements.txt
# download and unzip env
```
## Catalyst.RL
To train agents on the Animal Olympics environment, we can run Catalyst.RL as usual.
```bash
# start db node
redis-server --port 12012
# start trainer node
export GPUS="" # like GPUS="0" or GPUS="0,1" for multi-gpu training
CUDA_VISIBLE_DEVICES="$GPUS" catalyst-rl run-trainer --config ./configs/_exp_common.yml ./configs/ppo.yml
# start sampler node
CUDA_VISIBLE_DEVICES="" catalyst-rl run-samplers --config ./configs/_exp_common.yml ./configs/ppo.yml --sampler-id=1
# view tensorboard logs
CUDA_VISIBLE_DEVICE="" tensorboard --logdir=./logs
```
For more information about Catalyst.RL you can check [official repo](https://github.com/catalyst-team/catalyst), [documentaiton](https://catalyst-team.github.io/catalyst/) and [examples](https://github.com/catalyst-team/catalyst/tree/master/examples/rl_gym).