Prob-IRM

This is the source code for the KR2024 paper "Learning Robust Reward Machines from Noisy Labels". Please reach out to the main author, Roko Parać ([email protected]), for any questions regarding this source code or the paper itself.

Dependencies

Setting up a conda environment

Create a conda environment with the required python version

conda create -n rm_marl python=3.10.12
conda activate prob-irm
pip install -r requirements.txt

Installing ILASP

ILASP is used for automata learning. You can install it by adding its binary to the system PATH. Binaries can be found here: https://github.com/ilaspltd/ILASP-releases/releases. The experiments have been run with v4.4.0

We recommend downloading the binary to $HOME/bin for consistency with this source code. This directory can be added to path with the following command: export PATH="$HOME/bin:$PATH"

Installing dot

dot is used for generating automaton pdfs.

The dot on Ubuntu is installed with:

sudo apt install graphviz

The experiments have been run with version 2.43.0, although a wide variety of versions should probably work since its only purpose was to generate pdfs.

Running pre-defined experiments

The scripts directory contains the scripts for running all the experiments demonstrated in the paper. These have been run on Imperial's RCS (Research Compute Service) cluster. It is setup as a PBS cluster, but we have also attempted to use Condor and SLURM.

To run the standard Deliver Coffee experiment:

1. cd scripts/
2. ./deliver_coffee.sh

Runnning custom experiments

The file dqrm_coffee_world.py is the starting point of our program. It is set up using Hydra configuration management tool (https://hydra.cc/). To see all the available configuration options run:

python dqrm_coffee_world.py --cfg job

These can be overriden from the command line, for example to change the number of episodes to 4000:

python dqrm_coffee_world.py run.total_episodes=4000

Please reach out if you are struggling to run an experiment.