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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Temporally Interpretable Differentiable Decision Trees
Oct 7, 2026Eisuke Hirota, Aarav Sane, Rohan PalejaInterpretable ML Interpretability in RL
Department of Computer Science, Purdue University · Thomas Lord Department of Computer Science, University of Southern California
Towards the Automatic Synthesis of Interpretable Chess Tactics
Oct 6, 2026Abhijeet Krishnan, Chris MartensGame-Playing Agents Chess
North Carolina State University Venture IV, 1730 Varsity Dr, Raleigh, NC 27606
Learning Explainable Representations of Complex Game-playing Strategies
Oct 6, 2026Abhijeet Krishnan, Colin M. Potts, Arnav Jhala +3Game-Playing Agents Program Synthesis
Venture IV 420, North Carolina State University, Raleigh, NC 27606 USA · Department of Aerospace Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, Maharashtra, India · Tata Research Development and Design Centre, Pune 411013, Maharashtra, India +1
Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football
Oct 5, 2026Kenjiro Ide, Taiga Someya, Kohei Kawaguchi +1Sequential Decision Making Interpretability in RL
Graduate School of Informatics, Nagoya University, Nagoya, Japan. · Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan. · Department of Economics, The Hong Kong University of Science and Technology, Hong Kong, China. +1
Escaping Local Views: Discovering Latent Concepts for Interpretable Multi-Agent Reinforcement Learning
Sep 28, 2026Yijie Sun, Sanquan Sun, Yanda Zhu +3Reinforcement Learning Partially Observable RL
Nanjing University · Hong Kong Polytechnic University · Shenzhen University
Learning and interpreting policies for simultaneous entanglement requests in quantum networks
Sep 24, 2026Leon Rode, Sumeet Khatri, Supartha PodderInterpretability in RL
Department of Computer Science, Stony Brook University · Department of Computer Science and Center for Quantum Information Science and Engineering, Virginia Tech
Auditability Is Not One Property: Rule Overlap, Behavioural Agreement, and Composition in Reinforcement Learning
Sep 23, 2026Liu Hung MingReinforcement Learning AI Agent Auditing
PARRAWA AI
Control-Diverse Reinforcement Fine-Tuning: Decoupling the Shared Control Bottleneck of RL Post-Training
Aug 8, 2026Binwen Tan, Jingchao Wang, Dengzhe Hou +6RL for Language Model Reasoning Multi-Task RL
1Graduate School of Information Sciences, Tohoku University · School of Computer Science, Peking University · 2Unprecedented-scale Data Analytics Center, Tohoku University +3
Interpretable reinforcement learning with decision-tree pruning
Aug 7, 2026Mark Leon Ringer, Michel TokicInterpretability in RL
Faculty of Mathematics, Informatics and Statistics, Ludwig-Maximilians-University Munich, Munich, Germany · Siemens AG, Data & Artificial Intelligence, Otto-Hahn-Ring 6, 81739 Munich, Germany
Does Latent Context Help? A Controlled Evaluation of Inverse Reinforcement Learning in Arctic Shipping
Aug 6, 2026Vaishnav Vaidheeswaran, Dilith Jayakody, Biruk Ambaw +3Reinforcement Learning Meta-Reinforcement Learning
Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada · Department of Industrial Engineering, Faculty of Engineering, Dalhousie University, Halifax, NS, Canada
dtControl2+ $\varepsilon$ : Trading Optimality for Explainability in MDPs via Decision Trees
Jul 28, 2026Tereza Kinská, Jan Křetínský, Tobias Meggendorfer +2Markov Decision Processes Markov Models
Masaryk University, Brno, Czech Republic · Technical University of Munich, Munich, Germany · Lancaster University Leipzig, Leipzig, Germany +1
SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning
Jul 28, 2026Tamar Gozlan, Claudia V. GoldmanInterpretable ML Interpretability in RL
Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem · Hebrew University Business School, Data Science Department, The Hebrew University of Jerusalem
Interpretable GOHR Agents via Sparse Autoencoders
Jul 27, 2026Shiwei Tan, Yusong Zhao, Weiyi Qin +6Transformer Interpretability Sparse Autoencoders
1Rutgers University · 3Paul B Kantor Consultant · University of Illinois Urbana-Champaign
Explainable Reinforcement Learning via Physics-Aware Policy Distillation
Jul 27, 2026Shaker Al-Tamari, Waled KadourRL Control Interpretability in RL
Dept. of Electrical and Computer Engineering RPTU Kaiserslautern-Landau Kaiserslautern, Germany
Sparse Gaussian-Mixture-Model Q-Functions via Hadamard Overparametrization for Online Reinforcement Learning
Jul 26, 2026Minh Vu, Konstantinos SlavakisGaussian Mixture Models Interpretability in RL
Institute of Science Tokyo, Department of Information and Communications Engineering, Yokohama, Japan.
Explainable Reinforcement Learning for assisting Air Traffic Controllers
Jul 24, 2026Anduel Mehmeti, Gabriella Gigante, Salvatore VenticinqueReinforcement Learning Intelligent Transportation Systems
Department of Engineering, University of Campania "Luigi Vanvitelli", Italy · CIRA, Italy
Emergent Compositional Skills in Mixture-of-Experts VLAs
Jul 22, 2026Shlok Shah, Rhiaan Jhaveri, Tharun Kumar Tiruppali Kalidoss +3Robot Policy Learning Robot Skill Learning
Department of Computer Science, Princeton University, Princeton, NJ, USA · Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA
Explaining Reinforcement Learning Agents via Inductive Logic Programming
Jul 15, 2026Celeste Veronese, Edoardo Zorzi, Daniele Meli +1Interpretability in RL Explainability Evaluation
University of Verona, Italy · Sapienza University of Rome, Italy
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
Jul 8, 2026Ignacio D. Lopez-Miguel, Ezio Bartocci, Thomas Eiter +1Reinforcement Learning Interpretable ML
TU Wien Vienna, Austria
Explainable Reinforcement Learning for Adaptive Traffic Signal Control
Jul 4, 2026Dickens Kwesiga, Nishu Choudhary, Angshuman Guin +1Reinforcement Learning Traffic Signal Control
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
What Probing Reveals about Autonomous Driving: Linking Internal Prediction Errors to Ego Planning
Jun 30, 2026Hyeonchang Jeon, Kyungbeom Kim, Eugene Vinitsky +1Autonomous Driving Safety Evaluation Autonomous Driving Planning
1Gwangju Institute of Science and Technology (GIST) · 2New York University
Hierarchical Reinforcement Learning in StarCraft Micromanagement with Influence Maps and Cluster-based Scripts
Jun 29, 2026Chunhui Bai, Changhe Li, Dequan Li +2Hierarchical RL Sparse-Reward RL
School of Artificial Intelligence and Automation, China University of Geosciences, Wuhan 430074, China · State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science & Technology, Huainan 232001, China · School of Artificial Intelligence, Anhui University of Science & Technology, Hefei 231131, China +1
SAT-RTS: A systematic framework for tactical knowledge extraction and visualization-based analysis in real-time strategy games
Jun 29, 2026Chunhui Bai, Changhe Li, Yuqiang Li +2Game-Playing Agents Interpretability in RL
School of Artificial Intelligence and Automation, China University of Geosciences, Wuhan 430074, China · Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China · Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China +2
Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See
Jun 23, 2026Mohamed Benabdelouahad, Ahmed Djalal Hacini, Nadir Farhi +1Reward Shaping RL for Autonomous Driving
National School of Artificial Intelligence (ENSIA), Sidi Abdellah Campus, Algiers, Algeria. · Cosys-Grettia, Univ Gustave Eiffel, F-77454 Marne-la-Vallee, France.
Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback
Jun 23, 2026Andreas Chouliaras, Luke Connolly, Dimitris ChatzpoulosReward Modeling Human-in-the-Loop AI
School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland
Objective-Behavior Alignment: Diagnostics for MORL Policy Selection
Jun 19, 2026Antonio Mone, Zuzanna Osika, Florian Felten +4Multi-Objective Reinforcement Learning Reinforcement Learning
Delft University of Technology, the Netherlands · ETH Zurich, Switzerland & herve.review, Belgium · ETH Zurich, Switzerland
Distilling LLM Reasoning into an Interpretable Policy Tree for Human-AI Collaboration
Jun 7, 2026Beiwen Zhang, Yongheng Liang, Guowei Zou +2Reinforcement Learning Interpretability in RL
Sun Yat-sen University
Learning Explicit Behavioral Models with Adaptive Questions and World-Model Probes
Jun 5, 2026Hikaru Shindo, Yu Deng, Teng Cao +5Reinforcement Learning World Models
1Artificial Intelligence and Machine Learning Lab, Technical University of Darmstadt, Germany · Department of Computer Science, Technical University of Darmstadt, Germany · 2Hessian Center for Artificial Intelligence (hessian.AI), Germany +3
Explainably Safe Reinforcement Learning
Jun 3, 2026Sabine Rieder, Stefan Pranger, Debraj Chakraborty +2Reinforcement Learning Interpretability in RL
Masaryk University · Technical University of Munich · Graz University of Technology
Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
Jun 1, 2026Hallah Shahid Butt, Qiong Huang, Gökhan Demirel +6RL Control Reinforcement Learning
Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany

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Temporally Interpretable Differentiable Decision Trees

Eisuke Hirota, Aarav Sane, Rohan Paleja

Department of Computer Science, Purdue University · Thomas Lord Department of Computer Science, University of Southern California

Interpretable ML Interpretability in RL Reinforcement Learning Policy Gradient Methods

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Interpretability offers a solution to safe autonomy by providing transparency into an agent's underlying decision-making model. Within sequential-decision making tasks, differentiable decision trees (DDTs) are one approach to such interpretability, maintaining automatic-differentiable policies while providing humans with a discrete tree-based visualization. Nonetheless, current implementations of DDTs are not well-suited for sequential-decision making domains, as there exists an inherent mismatch between a tree's single-timestep behavior and a human's multi-timestep planning. Our work thus introduces time as a new dimension of interpretability, coined as temporal interpretability, and demonstrates how temporal abstractions via action chunking improve it. We achieve this by first introducing two novel policy gradient algorithms that incorporate action chunking. Additionally, to maintain parameter-efficient trees, we develop an information-theoretic tree restructuring algorithm that modifies the tree during training. Across four simulation environments, we find that warm-starting action chunked DDTs from a distilled action chunked policy is the most effective way to obtain temporally interpretable trees: they match neural network policies in three of the four domains while using up to 80

\%

fewer parameters. Our code is available at https://github.com/ei5uke/temp-interp.

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