Shielded_DMARL

This repository contains the code for the composed shielding experiments with maddpg Safe multi-agent reinforcement learning via shielding paper.

Prerequisites:

• Python 3.6+
• gym
• matplotlib 3.0.0
• particle environment for deep MARL experiments (modified to be discretized + scenarios - code missing due to computer problems).
• shields for the particle environment synthesized using the Slugs tool, not provided because of file sizes.

Code structure:

• GridShield.py: contains the implementation of the composed shielding method currently restricted to 2 agents per shield but code can be modified to accomodate more.
• train_maddpg.py: train for a given scenario and record information with shielding using composed shielding option.
• train_test.py: train then run testing phase (no learning or exploration) and record relevant information.
• /logs: contains the output logged
• /policy: contains policy checkpoints for maddpg
• /learning_curves: contains relevant info for graphing (rewards and collisions).
• /benchmark_files: contains info pertaining to collisions for shielding and without shielding.

Notes:

• Code is provided as is and not actively maintained at the moment. However, I am happy to answer questions.