This project implements CAPS: Comprehensible Abstract Policy Summaries
Joe McCalmon, Thai Le, SarraAlqahtani, Dongwon Lee, “CAPS: Comprehensible Abstract Policy Summaries for Explaining Reinforcement Learning Agents”, The 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS2022), accepted
To run CAPS for an implemented environment:
pip install -r requirements.txt
python run.py --env='env_name' --path='model_path' --other_flags=...
Ex: python run.py --env=mountain --path=./MountainCar/checkpoints/checkpoint-580 --num_episodes=3 --alg=PPO
To run CAPS with a new environment:
- Train the environment separately. Save the tensorflow or pytorch model.
- Implement a function which will run the agent in testing and return the dataset, D, specified in the paper.
- Alter run.py to include your test function, and handle env and path flags appropriately.
- Create a class in translation.py that includes your user-predicates, following the template laid out in the code.
- Run CAPS