Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications

Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning With Shapley Values

(2022) Alexandre Heuillet et al.   IEEE Computational Intelligence Magazine

Explainable Deep Reinforcement Learning: State of the Art and Challenges

(2022) George A. Vouros   ACM COMPUTING SURVEYS

MoËT: Mixture of Expert Trees and its application to verifiable reinforcement learning

(2022) Marko Vasić et al.   NEURAL NETWORKS

Event-driven temporal models for explanations - ETeMoX: explaining reinforcement learning

(2021) Juan Marcelo Parra-Ullauri et al.   Software and Systems Modeling

Explainable AI in Deep Reinforcement Learning Models for Power System Emergency Control

(2021) Ke Zhang et al.   IEEE Transactions on Computational Social Systems

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

(2020) Wang Chang et al.   Chinese Journal of Aeronautics

Deep Reinforcement Learning for Safe Local Planning of a Ground Vehicle in Unknown Rough Terrain

(2020) Shirel Josef et al.   IEEE Robotics and Automation Letters

Explaining Deep Learning Models Through Rule-Based Approximation and Visualization

(2020) Eduardo Soares et al.   IEEE TRANSACTIONS ON FUZZY SYSTEMS