A keras solution for 2nd place NIPS RL 2017 challenge.
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)
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.
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 the model in parallel and calculate the average score.
We provide you with some trained parameters .
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
python test.py --help to get more information .