Neuronal Circuit Policies

Official code repository to verify and reproduce the experiments of the paper Re-purposing Compact Neuronal Circuit Policies to Govern Reinforcement Learning Tasks

@article{hasani2018repurposing,
  title={Re-purposing Compact Neuronal Circuit Policies to Govern Reinforcement Learning Tasks},
  author={Hasani, Ramin M. and Lechner, Mathias  and  Amini, Alexander and Rus,Daniela and Grosu, Radu},
  journal={arXiv preprint arXiv:1809.04423},
  year={2018}
}

How to get it working:

• First you need to install the Reinforcement Learning environments: Open-AI Gym Roboschool and rllab
• Next you need to compile the pybnn library (simulates neuronal circuits using C++) and copy the shared object to each of the local working directories. You have to install python-boost:

sudo apt-get install libboost-python-dev

and then compile and copy the library:

cd pybnn
make
cp bin/pybnn.so ../cartpole_rllab/
cp bin/pybnn.so ../mountaincar_rllab/
cp bin/pybnn.so ../invpend_roboschool/
cp bin/pybnn.so ../mountaincar_gym/
cp bin/pybnn.so ../parking/

Verifying

To check if the toolchain is working as intended you can execute a learned neuronal policy:

cd mountaincar_gym/
python3 mountaincartw.py

or

cd invpend_roboschool/
python3 twcenterpend.py

Note: You need to cd into the particular directories because the library and the optimized policy parameters are loaded from relative paths of the working directory.

Parking

To get the deterministic parking environment working you have to compile the rover simulator:

cd parking/park_gym/
make
cp bin/pyparkgym.so ../
cd ..