Interpretability in RL

RL: Reinforcement Learning

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
May 31, 2026Federica Tonti, Ricardo VinuesaRL Control Reinforcement Learning
Department of Aerospace Engineering University of Michigan Ann Arbor, MI 48109, United States
Interpretable Policy Distillation for Power Grid Topology Control
May 30, 2026Aleksandra Dmitruka, Karlis FreivaldsRL Control Reinforcement Learning
University of Latvia, Faculty of Exact Sciences and Technology, Riga, Latvia
DiPRL: Learning Discrete Programmatic Policies via Architecture Entropy Regularization
May 18, 2026Chengpeng Hu, Yingqian Zhang, Hendrik BaierReinforcement Learning Program Synthesis
Eindhoven University of Technology, Eindhoven, the Netherlands · Centrum Wiskunde & Informatica, Amsterdam, the Netherlands
Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models
May 14, 2026Senne Deproost, Denis Steckelmacher, Ann NowéReinforcement Learning Vector Quantization
Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Interpreting Reinforcement Learning Agents with Susceptibilities
May 8, 2026Chris Elliott, Einar Urdshals, David Quarel +1Reinforcement Learning Neural Network Interpretability
Timaeus
Hierarchical Support Vector State Partitioning for Distilling Black Box Reinforcement Learning Policies
May 5, 2026Senne Deproost, Mehrdad Asadi, Ann NowéReinforcement Learning Interpretability in RL
aVrije Universiteit Brussel · bFlanders Make
Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters
May 4, 2026Lingxiao Kong, Cong Yang, Oya Deniz Beyan +1RL for Robotics Reinforcement Learning
Fraunhofer Institute for Applied Information Technology FIT, Germany · Soochow University, China · University of Cologne, Germany +1
Interpretable experiential learning based on state history and global feedback
May 1, 2026Anton KoloninReinforcement Learning Game-Playing Agents
1The Artificial Intelligence Research Center, Novosibirsk State University, Russia · 2Aigents, Russia.
Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models
Apr 27, 2026Dan Shi, Zhuowen Han, Simon Ostermann +3Reinforcement Learning RL for Language Model Reasoning
TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China · German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany · Saarland University, Saarbrücken, Germany
Task-specific Subnetwork Discovery in Reinforcement Learning for Autonomous Underwater Navigation
Apr 23, 2026Yi-Ling Liu, Melvin Laux, Mariela De Lucas Alvarez +2Reinforcement Learning Underwater Robotics
Robotics Innovation Center, German Research Center for Artificial Intelligence, Bremen, Germany · Robotics Research Group, University of Bremen, Bremen, Germany
Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning
Nov 25, 2025Charlotte Beylier, Hannah Selder, Arthur Fleig +2Reinforcement Learning Interpretability in RL
Max Planck Institute for Human Cognitive and Brain Sciences · Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig · Leipzig, Germany +2

May 31, 2026, cs.LGJ/K move · Enter open · S save

Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction

Federica Tonti, Ricardo Vinuesa

Department of Aerospace Engineering University of Michigan Ann Arbor, MI 48109, United States

RL Control Reinforcement Learning Interpretability in RL Computational Fluid Dynamics

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We propose a method combining Multi-Agent Deep Reinforcement Learning (MARL) and eXplainable Deep Learning (XDL) to reduce drag in wall-bounded turbulent flows. Taking as a baseline the results of training agents directly targeting wall-shear stress and opposition control, three SHAP-guided approaches are compared. In the first, the reward is computed from SHAP attributions of a U-net predicting the future velocity field; in the second, from SHAP attributions of a U-net predicting the skin-friction coefficient; in the third, from a combination of SHAP attributions of two U-nets predicting the skin-friction coefficient and the wall pressure fluctuations, respectively. The combined SHAP strategy based on skin-friction coefficient and wall-pressure fluctuations achieves the best overall performance, achieving a DR of 34.44% and a NES of 34.01% with only 0.43% normalized input power. Relative to opposition control, drag reduction and net energy saving increase by 49.41% and 48.52%, respectively. Compared with the direct wall-shear-stress baseline, the proposed strategy simultaneously improves performance while reducing the normalized actuation cost from 5.90% to 0.43%. Analysis of the results reveals that the energetically efficient policy is consistent with pressure-gated actuation, activating predominantly at near-zero wall pressure, and operates on a temporal timescale comparable to the lifetime of the near-wall turbulent structures.

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