Implementation of the compositional RL (DiRL) approach presented in the paper "Compositional Reinforcement Learning from Logical Specifications", Kishor Jothimurugan, Suguman Bansal, Osbert Bastani and Rajeev Alur. In NeurIPS 2021.
Requires Python version 3.7 or higher. Commands to install all dependencies:
pip install numpy
pip install gym
pip install torch torchvision
pip install matplotlib
pip install opencv-python
pip install mujoco-py==2.0.2.8
Mujoco-py requires mujoco physics simulator which can be installed following the instructions here.
To run DiRL on 9Rooms,
python -m spectrl.examples.9rooms_dirl -e 2 -n {run_number} -d {directory} -s {spec_number}
Results are stored in the {directory} provided and {run_number} is an integer used to distinguish different runs for the same specification. {spec_number} is either 3, 4, 5, 6 or 7 corresponding to specs 1 to 5 in the paper.
Similarly, for 16Rooms (with all doors open), the command is
python -m spectrl.examples.16rooms_dirl -e 3 -n {run_number} -d {directory} -s {spec_number}
{spec_number} is either 9, 10, 11, 12 or 13 corresponding to specs 1 to 5 in the paper. To train on the 16Rooms environment with some blocked doors, use -e 4 instead of -e 3.
For the fetch environment, the command is
python -m spectrl.examples.fpp_dirl -n {run_number} -d {directory} -s {spec_number}
Here {spec_number} is either 3 (PickAndPlace), 4 (PickAndPlaceStatic) or 5 (PickAndPlaceChoice). Use the option -g to run on GPU if available.
Replace _dirl in the above commands with (i) _spectrl to run spectrl baseline or (ii) _tltl to run tltl baseline.