Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/hzwer/NIPS2017-LearningToRunACE
2nd place solution of NIPS2017 LearningToRun Competition.
https://github.com/hzwer/NIPS2017-LearningToRunACE
deep-learning keras reinforcement-learning
Last synced: 3 months ago
JSON representation
2nd place solution of NIPS2017 LearningToRun Competition.
- Host: GitHub
- URL: https://github.com/hzwer/NIPS2017-LearningToRunACE
- Owner: hzwer
- Created: 2017-11-24T06:54:22.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-05-29T10:09:07.000Z (over 2 years ago)
- Last Synced: 2024-11-01T17:02:34.044Z (3 months ago)
- Topics: deep-learning, keras, reinforcement-learning
- Language: Python
- Homepage:
- Size: 14.2 MB
- Stars: 125
- Watchers: 7
- Forks: 28
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# NIPS2017-LearningToRun with ACE
[Zhewei Huang](https://scholar.google.com/citations?user=zJEkaG8AAAAJ&hl=zh-CN&oi=sra), [Shuchang Zhou](https://scholar.google.com/citations?user=zYI0rysAAAAJ&hl=zh-CN&oi=sra), BoEr Zhuang, [Xinyu Zhou](https://scholar.google.com/citations?user=Jv4LCj8AAAAJ&hl=zh-CN&oi=ao)
A keras solution for 2nd place [NIPS RL 2017 challenge](https://www.crowdai.org/challenges/nips-2017-learning-to-run/leaderboards?challenge_round_id=12).
There is a [slide](https://docs.google.com/presentation/d/1dgXDFlr62jQ-OdEoYVCGwuUgux3u-jrMaXVp94OVOSk/edit?usp=sharing), a [lecture](https://drive.google.com/open?id=15_XBOms-T1G1jeiDm7xGTn2JGQ2FviT5) and a [writeup(arxiv)](https://arxiv.org/abs/1712.08987) about our work.
## To Run
### preparation
These instructions expect that opensim-rl conda environment is already setup as described in : https://github.com/stanfordnmbl/osim-rl/ .
```
$ source activate opensim-rl
```
Other dependencies is needed as follow
* Keras(since old version does not support selu activation)
* TensorFlow
* matplotlib
* numpy
* Pyro4
* parse
* pymsgbox(optional)
### parallelism
This version requires farming, before starting `train.py`, you should first start some farms by running `python farm.py` on each SLAVE machine you own. Then create a `farmlist.py` in the working directory (on the HOST machine) with the following content :
```
farmlist_base = [('127.0.0.1', 4), ('192.168.1.1', 8)]
# a farm of 4 cores is available on localhost, while a farm of 8 is available on another machine.
# expand the list if you have more machines.
# this file will be consumed by the host to find the slaves.
```
Try `python farm.py --help` to get more information about how to set the environment.
More information can be found in https://github.com/ctmakro/stanford-osrl .
Thanks to @ctmakro for providing us with this frame.
### test
Test the model in parallel and calculate the average score.
We provide you with some [trained parameters](https://drive.google.com/open?id=10RDVQA5zjUjNXz7Igak3k92_s_XKI2Uw).
```
python test.py -a=10 -c=5 -t=200 -p logs
# test the model for 200 times with 10 actor networks and 5 critic networks ensemble
# the network parameters should be placed as logs/actormodel1.h5 ... logs/actormodel10.h5
```
Try `python test.py --help` to get more information .
## Contributors
- [hzwer](https://github.com/hzwer)
- [floz](https://github.com/NewGod)
## Resources
- [Official YouTube demos](https://www.youtube.com/watch?v=rhNxt0VccsE)
- [赛后专访](https://www.leiphone.com/news/201711/b2OfTdcMUmpYKx6S.html)
- Also a teaser video from the winning team https://www.bilibili.com/video/BV1jE411B74u