Read the accompanying blog post here (tbd).
Verifiability via Iterative Policy ExtRaction (2019) paper]
In this paper the authors distill a Deep Reinforcement Learning such as DeepQN into a decision tree policy which can then be automatically checked for correctness, robustness, and stability.
This repository implements and tests the viper algorithm on the following environments:
- CartPole
- Atari Pong
- ToyPong (tbd)
The entire project can be run using the main.py script which can take more options than the ones mentioned below.
To get a full list of options run python main.py --help.
The commands below reflect configurations that helped achieve a perfect reward averaged over 50 rollouts.
Atari Pong (TODO: only achieves 20.12 +/- 1.66 out of 21):
python main.py train-oracle --env-name PongNoFrameskip-v4 --n-env 64 --total-timesteps 15_000_000
Toy Pong:
python main.py train-oracle --env-name ToyPong-v0 --n-env 1 --total-timesteps 1_000_000
Cart pole:
python main.py train-oracle --env-name CartPole-v1 --n-env 8 --total-timesteps 100_000
You can always resume training a stored model by adding the --resume flag to the same command.
Once the oracle policies are trained you can run viper on the same environment:
Cart pole:
python main.py train-viper --env-name CartPole-v1 --n-env 1
Toy Pong:
python main.py train-viper --env-name ToyPong-v0 --n-env 4 --max-leaves 61 --total-timesteps 1_000_000