Neuronal Circuit Policies

How to get it working:

• First you need to install the Reinforcement Learning environments: Open-AI Gym Roboschool and mujoco-py
• 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 ../invpend_roboschool/
cp bin/pybnn.so ../mountaincar_gym/
cp bin/pybnn.so ../half_cheetah/
cp bin/pybnn.so ../parking/

Verifying

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

cd twmountaincar/
python3 twmountaincar.py --file tw-optimized.bnn

or

cd invpend_roboschool/
python3 twcenterpend.py --file tw-optimized.bnn

or

cd half_cheetah/
python3 half_cheetah.py --file tw-optimized.bnn

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 ..

Random circuits

To generate random circuits that have the same size as the TW circuit run

cd generate_circuits/
python3 generate_circuit.py

Other NN architectures (MLP, LSTM)

To train a MLP or LSTM on the tasks by our Adaptive Random Search run

cd other_nn_architectures/
python3 nn_run_env.py --env [invpend|cheetah|mountaincar]