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Inverse Constrained Reinforcement Learning

arXiv 2011.09999 ↗First posted 19 Nov 2020cs.LG cs.RO cs.SYcited by 45

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Abstract (arXiv)

In real world settings, numerous constraints are present which are hard to specify mathematically. However, for the real world deployment of reinforcement learning (RL), it is critical that RL agents are aware of these constraints, so that they can act safely. In this work, we consider the problem of learning constraints from demonstrations of a constraint-abiding agent's behavior. We experimentally validate our approach and show that our framework can successfully learn the most likely constraints that the agent respects. We further show that these learned constraints are textittransferable to new agents that may have different morphologies and/or reward functions. Previous works in this regard have either mainly been restricted to tabular (discrete) settings, specific types of constraints or assume the environment's transition dynamics. In contrast, our framework is able to learn arbitrary textitMarkovian constraints in high-dimensions in a completely model-free setting. The code can be found it: urlhttps://github.com/shehryar-malik/icrl.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Nov 2020.

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Citing papers per year: 2021: 2; 2022: 7; 2023: 7; 2024: 13; 2025: 6; 2026: 10.

45 papers.

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  1. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
  2. 2022Sustainable Online Reinforcement Learning for Auto-biddingcited by 28
  3. 2023Learning Safety Constraints from Demonstrations with Unknown Rewardscited by 19
  4. 2022Benchmarking Constraint Inference in Inverse Reinforcement Learningcited by 18
  5. 2023Identifiability and Generalizability in Constrained Inverse Reinforcement Learningcited by 16
  6. 2022Learning Soft Constraints From Constrained Expert Demonstrationscited by 13
  7. 2023An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradientcited by 12
  8. 2024A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challengescited by 10
  9. 2021Making Human-Like Trade-offs in Constrained Environments by Learning from Demonstrationscited by 7
  10. 2023Maximum Causal Entropy Inverse Constrained Reinforcement Learningcited by 7
  11. 2024A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimizationcited by 7
  12. 2025Modeling Others' Minds as Codecited by 6
33 more
  1. 2024Safety through feedback in Constrained RLcited by 5
  2. 2024An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learningcited by 5
  3. 2022X-MEN: Guaranteed XOR-Maximum Entropy Constrained Inverse Reinforcement Learningcited by 4
  4. 2022TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasetscited by 4
  5. 2024Confidence Aware Inverse Constrained Reinforcement Learningcited by 4
  6. 2024Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcarecited by 4
  7. 2022Formalizing the Problem of Side Effect Regularizationcited by 3
  8. 2024Situated Instruction Followingcited by 3
  9. 2021How To Not Drive: Learning Driving Constraints from Demonstrationcited by 2
  10. 2022Learning Behavioral Soft Constraints from Demonstrationscited by 2
  11. 2025Leveraging Constraint Violation Signals For Action-Constrained Reinforcement Learningcited by 2
  12. 2023Inverse Reinforcement Learning With Constraint Recoverycited by 1
  13. 2024Imitating Cost-Constrained Behaviors in Reinforcement Learningcited by 1
  14. 2024Provably Efficient Exploration in Inverse Constrained Reinforcement Learningcited by 1
  15. 2023Learning Safety Constraints From Demonstration Using One-Class Decision Treescited by 0
  16. 2024Multi Task Inverse Reinforcement Learning for Common Sense Rewardcited by 0
  17. 2024Safe Reinforcement Learning with Learned Non-Markovian Safety Constraintscited by 0
  18. 2024Positive-Unlabeled Constraint Learning for Inferring Nonlinear Continuous Constraints Functions from Expert Demonstrationscited by 0
  19. 2024Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination Gamescited by 0
  20. 2025A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Modelscited by 0
  21. 2025TraCeS: Learning Per-Timestep Constraint-Violation Credit from Sparse Trajectory-Level Labelscited by 0
  22. 2025Learning safe, constrained policies via imitation learning: Connection to Probabilistic Inference and a Naive Algorithmcited by 0
  23. 2025Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learningcited by 0
  24. 2026Demonstration-Guided Observation Attacks on Black-Box Safe Reinforcement Learning Controllers for Robotic Systemscited by 0
  25. 2026Behavior Learning (BL): Learning Hierarchical Optimization Structures from Datacited by 0
  26. 2026Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspectivecited by 0
  27. 2026Safe Reinforcement Learning with Preference-based Constraint Inferencecited by 0
  28. 2026Multi-Objective Constraint Inference using Inverse reinforcement learningcited by 0
  29. 2026Implicit Safety Alignment from Crowd Preferencescited by 0
  30. 2026Constrained Multi-Objective Reinforcement Learning with Max-Min Criterioncited by 0
  31. 2026Reading Cognition as Decisions Unfold in Words: A Factorized Inverse Decision Modelcited by 0
  32. 2026Calibration-risk routing for controlled world-model adaptationcited by 0
  33. 2026Watch, Infer, Coordinate: Inferring Robot Partner Constraints for Zero-Shot Coordinationcited by 0