Constrained RL

RL: Reinforcement Learning

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22 papers in the last four weeks, up 175% on the four weeks before. 0.2% of all new papers.

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Latest papers 143

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FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems
Oct 8, 2026Songyuan Zhang, Baljeet Singh, Sarthak Ranjeet Kaingade +2Safe RL Constrained RL
Massachusetts Institute of Technology · Amazon
A Unified Bellman Operator for Safety-Critical Reinforcement Learning
Oct 8, 2026Nishanth Arun Rao, Royina Karegoudra Jayanth, Benjamin Eysenbach +1Safe RL Constrained RL
Department of Electrical and Computer Engineering Princeton University · Department of Computer Science Princeton University
Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints
Oct 8, 2026Qian Zuo, Francesco Emanuele StradiMarkov Decision Processes Constrained RL
University of Edinburgh · Politecnico di Milano
Constrained Command-Conditioned Reinforcement Learning with Bandit Strategy Selection in Real-Time Strategy Games
Oct 8, 2026Nick Leenders, Roy Lindelauf, Joost van Oijen +1Reinforcement Learning Game-Playing Agents
NLR Royal Netherlands Aerospace Centre Amsterdam, The Netherlands · Data Science Center of Excellence Faculty of Military Sciences, Breda, NL · Departement Intelligent Systems Tilburg University, Tilburg, NL
Safe on Average, Unsafe in the Tail: When Is the Episodic-Cost Tail Controllable?
Oct 7, 2026Samuel Tetteh, Cody FlemingSafe RL Constrained RL
Iowa State University Ames, Iowa, USA
Learning Unknown Constraints without Unsafe Data via Optimality and Counterfactual Regularization
Oct 7, 2026Zhouyu Zhang, Chih-Yuan Chiu, Glen ChouConstrained RL Learning from Demonstration
School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, GA 30309 · School of Cybersecurity and Privacy School of Aerospace Engineering Georgia Institute of Technology
Who Bears the Burden? Learning Responsibility for Shared Constraints in Multi-Agent Reinforcement Learning
Oct 5, 2026Xiaoyang Cao, Jingqi Li, Zhe Fu +1Game Theory Constrained RL
Massachusetts Institute of Technology · The University of Texas at Austin · Stanford University +1
Constrained Goal-directed Planar Graph Generation with Grammar-based Reinforcement Learning
Oct 5, 2026Nicolas Hochuli, Lorenzo Miele, Kristina Shea +1Constrained Generative Modeling Graph Generation
Engineering Design and Computing Laboratory ETH Zürich
Reachability-Aware Diffusion Policy Optimization
Oct 5, 2026Hikmet Simsir, Kutay Demiray, Ozgur S. OguzConstrained RL Diffusion Policy
Department of Computer Engineering, Bilkent University
Rate-Optimal Algorithm for Adversarial Linear CMDPs
Oct 1, 2026Kihyun Yu, Honghao Wei, Dabeen LeeRegret Minimization in RL Markov Decision Processes
KAIST · Washington State University · Seoul National University
Learning Infinite-Horizon Average-Reward CMDPs via State Augmentation
Sep 30, 2026Kihyun Yu, Seoungbin Bae, Dabeen LeeRegret Minimization in RL Markov Decision Processes
KAIST · Seoul National University
VERA: Verifiable Feasibility Representations with Counterfactual Credit for Constrained Multi-Agent Control
Sep 30, 2026Bo Yin, Dongbo Li, Hongkai Chen +2Multi-Agent Control Constrained RL
Harbin Institute of Technology, Harbin, China · The Chinese University of Hong Kong, Hong Kong SAR, China
Brain-SAD: A Brain-Inspired Safe Autonomous Driving Control Framework with Dynamic Fear-Oriented Constraint on Dual-Policy
Sep 29, 2026Huan Rong, Chao Yin, Anouar Imel +2Constrained RL RL for Autonomous Driving
School of Artificial Intelligence, Nanjing University of Information Science & Technology, Jiangsu, Nanjing, 210-044, China · School of Information and Communication Engineering, University of Electronic Science and Technology of China, Sichuan, Chengdu, 611731, China · School of Software, Nanjing University of Information Science & Technology, 210044, Jiangsu, China +1
Learning to Harvest Without Collapse in a Regenerative Commons: A Lagrangian Framework
Sep 29, 2026Jose Tupayachi, Xueping Li, Soham DasMulti-Agent Control Constrained RL
Oak Ridge National Laboratory Department of Industrial and Systems Engineering, University of Tennessee, Knoxville · Department of Industrial and Systems Engineering, University of Tennessee, Knoxville
PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learning
Sep 28, 2026Zhanhua Pan, Xiao Liu, Zhilong Cao +2RL Benchmarks Constrained RL
Nanyang Technological University, Singapore. · Cornell University, USA. · University of Bristol, UK.
Safe Greenhouse Climate Control Using Lagrangian-Constrained PPO with Kolmogorov-Arnold Networks
Sep 28, 2026Hangzun Liu, Yuling Fan, Fang Tian +3Constrained RL Proximal Policy Optimization
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China · Yunnan Modern Agricultural Industry Research Institute Co., Ltd.,Kunming, Yunnan 650000, China · College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China +1
Finite-Time Concentration and Convergence Rates for Projected Two-Time-Scale Stochastic Approximation with Markov Noise
Sep 28, 2026Rahul Singh, Vivek S. Borkar, Eric MoulinesLast-Iterate Convergence Stochastic Approximation
Laboratoire de Recherche de l’EPITA, Paris, France · Indian Institute of Technology Bombay · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
Model-Informed Safe Reinforcement Learning for Bipedal Locomotion via Step-to-Step Prediction
Sep 28, 2026Victor Paredes, Ayonga HereidHumanoid Robot Locomotion Control Barrier Functions
Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH, USA.
Q-learning Penalized Transformer for Safe Offline Reinforcement Learning
Sep 28, 2026Shengchao Hu, Peng Wang, Jifeng Hu +5Reinforcement Learning Transformer-Based RL
Shanghai Jiao Tong University, China · Shenzhen Campus of Sun Yat-sen University, China · Jilin University, China +1
Vision--Language Signals in Constrained RL: Safety Gains Without Anticipation
Sep 28, 2026Samuel Tetteh, Cody FlemingConstrained RL RL for Autonomous Driving
Iowa State University Ames, Iowa, USA
Elucidating the Design Space of Regression-based Diffusion Reinforcement Learning
Sep 27, 2026Toyota Li, David Zhao, Alan ZhaoReinforcement Learning Constrained RL
Tencent
Safe Score Matching: Diffusion Policies with Hamilton-Jacobi Reachability for Online Safe Reinforcement Learning
Sep 27, 2026Boyang Li, Matthew Kim, Sylvia Lee HerbertScore Matching Constrained RL
University of California San Diego
Smoothness as a Constraint for Stable Humanoid Locomotion
Sep 21, 2026Utsav Panchal, Denis Kleyko, Unal Artan +1Robotic Control Humanoid Robot Locomotion
AI, Robotics and Cybersecurity Center (ARC) and Department of Computer Science, Örebro University, Sweden
Do Language Models Need Music Supervision? Verifiable Rewards for Multi-Constraint Symbolic Music Generation
Sep 20, 2026Haoyue Liu, Xiaoying TangMusic Generation Evaluation Controllable Music Generation
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China · Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)
Cost-Aware Reinforcement Learning with Action Masking and Projection for Battery Energy Storage Dispatch under Suppressed-Spread Market Shifts
Sep 20, 2026Kuanlin Chen, Chen-Wei Kuo, Cheng-En OuConstrained RL Proximal Policy Optimization
Independent Researcher Taoyuan, Taiwan · National Tsing Hua University Hsinchu, Taiwan · Independent Researcher
Safe Meta-Reinforcement Learning via Information Space Reachability
Sep 14, 2026Zeyang Li, Sunbochen Tang, Navid AzizanReachability Analysis Constrained RL
Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Evaluation Metrics for Safe Reinforcement Learning
Sep 14, 2026Lindsay Spoor, Aske Plaat, Thomas MoerlandRL Benchmarks Constrained RL
Leiden Institute of Advanced Computer Science, Leiden University, Leiden, the Netherlands
Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies
Sep 14, 2026Anna Rothenhäusler, Daniel Jost, Raghu Rajan +4Constrained RL Vehicle Dynamics
University of Freiburg, Freiburg, Germany · Bosch Center for Artificial Intelligence, Stuttgart, Germany · Coburg University of Applied Sciences, Coburg, Germany
A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning
Sep 14, 2026Amine Andam, Jamal Bentahar, Mustapha HedabouKL-Regularized RL Adversarial Robustness
Mohammed VI Polytechnic University · Khalifa University · Concordia University
Proactive Context-Forecasted Safety Constraints for Nonstationary Reinforcement Learning
Sep 8, 2026Tim TomashevskiyNon-Stationary RL Constrained RL
Department of Computing and Software McMaster University Hamilton, Ontario, Canada

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FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems

Songyuan Zhang, Baljeet Singh, Sarthak Ranjeet Kaingade, Chuchu Fan+1

Massachusetts Institute of Technology · Amazon

Safe RL Constrained RL Robot Safety

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Safe reinforcement learning commonly places safety and task performance in the same policy objective, where they can introduce competing updates. Safety filters separate them at action execution, but classical designs require an analytic safety function and dynamics model, and standard minimal-intervention filters are myopic to long-horizon task return because they minimize only instantaneous action deviation. Hard projections are also undefined when no safe action exists. We present FAITH, a feasibility-aware, model-free framework that approximates the optimal state-action safety value and amortizes minimal-intervention filtering with a feedforward network. The task policy optimizes the task return through the filtered dynamics, which recovers the feasible constrained problem without a competing safety term in the task-policy update. When no action satisfies the learned safety condition, the same filter approaches the action with minimum predicted peak harm. On a double integrator example and a Safety Gym environment, FAITH achieves the highest return among methods with no feasible-start violations and matches the lowest harm from infeasible starts. On a 29-DoF humanoid, it reaches a 99.95% safety rate while retaining 97% of the unfiltered return in Walking-Avoid, and obtains the highest measured safety rate in Push-Avoid by learning to sacrifice balancing and fall away from the protected region. The same policies are also demonstrated on a real-world Unitree G1 humanoid.

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Latest papers 143