XRL-Bench: A benchmark for Explainable Reinforcement Learning

XRL-Bench is a comprehensive benchmark suite for evaluating eXplainable Reinforcement Learning (XRL) methods. It provides a standard and unified platform for researches and practitioners to develop, test, and compare their state importance-based XRL algorithms. XRL-Bench includes a wide range of environments, explainers, and evaluation methods to facilitate the development of state-of-the-art XRL techniques.

See our KDD paper for details and citations.

Key Features

• Environments: XRL-Bench includes diverse open-source environments based on tabular state form, such as Dunk City Dynasty (only supported in Python3.8), Lunar Lander, CartPole, and Flappy Bird (supported in Python3.9 or higher version), and image state form, such as Breakout, Pong, covering a wide range of reinforcement learning tasks.
• Explainers: The benchmark supports various state importance-based explainers like TabularSHAP, TabularLIME, SARFA, Perturbation Saliency, Integrated Gradient, DeepSHAP and GradientSHAP, to understand the decision-making process of the RL agents.
• Evaluation Methods: XRL-Bench provides multiple evaluation methods, including Fidelity-focused methods such as AIM, AUM, PGI and PGU, and Stability-focused methods such as RIS.

Getting Started

To get started with XRL-Bench, follow the instruction below to set up the required dependencies

git clone https://github.com/fuxiAIlab/xrl-bench.git
cd xrl-bench
pip install -r requirements.txt

and run the example code

from xrlbench.explainers import Explainer
from xrlbench.evaluator import Evaluator
from xrlbench.environments import Environment

env = Environment(environment_name="LunarLander")
dataset = env.get_dataset()
actions = dataset['action']
states = dataset.drop(['action', 'reward'], axis=1)
explainer = Explainer(method="tabularShap", state=states, action=actions)
shap_values = explainer.explain()
evalutor = Evaluator(metric="AIM", environment=env)
aim = evaluator.evaluate(states, actions, shap_values, k=1)

LeaderBoards

We maintain two leaderboards to compare the AUC performance of explainers for both types of state form under various evaluation metrics.

Tabular states (DunkCityDynasty)

Explainer	AIM	AUM	PGI	PGU	RIS
TabularSHAP	0.214	0.894	0.905	0.662	1.023
DeepSHAP	0.337	0.493	0.790	0.712	1.261
GradientSHAP	0.326	0.523	0.766	0.690	1.419
Integrated Gradient	0.323	0.522	0.808	0.688	1.465
SARFA	0.361	0.709	0.952	0.665	1.071
Perturbation Saliency	0.364	0.687	0.951	0.680	1.357
TabularLIME	0.215	0.779	0.954	0.274	1.901

Tabular states (LunarLander)

Explainer	AIM	AUM	PGI	PGU	RIS
TabularSHAP	0.116	0.693	5.258	4.895	2.646
DeepSHAP	0.188	0.663	5.988	4.321	2.623
GradientSHAP	0.203	0.614	5.963	4.317	3.134
Integrated Gradient	0.203	0.618	5.930	4.375	2.800
SARFA	0.388	0.363	4.953	5.169	6.408
Perturbation Saliency	0.382	0.353	4.847	5.528	4.878
TabularLIME	0.323	0.472	6.179	3.755	2.871

Image states (Breakout)

Explainer	AIM	AUM	PGI	PGU	RIS
DeepSHAP	0.162	0.630	1.748	0.347	0.375
GradientSHAP	0.260	0.655	1.755	0.384	0.659
Integrated Gradient	0.292	0.652	1.812	0.364	0.109
SARFA	0.253	0.270	1.225	0.991	0.653
Perturbation Saliency	0.258	0.387	1.370	0.621	0.649

Contributing

We welcome contirbutions to XRL-Bench! If you'd like to contirbute, please follow these guidelines:

1. Fork the repository and create a new branch for your feature or bug fix.
2. Add your changes and include tests to ensure your changes work as expected.
3. Update the documentation as necessary.
4. Submit a pull request and wait for a review from the maintainers.

License

XRL-Bench is under MIT license.