# Confidence Aware Inverse Constrained Reinforcement Learning

arXiv: https://arxiv.org/abs/2406.16782 · HTML: https://mlsanity.com/paper/2406.16782/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Reinforcement learning based recommender systems: A survey](https://mlsanity.com/paper/2101.06286/) (2021)
- [Inverse Constrained Reinforcement Learning](https://mlsanity.com/paper/2011.09999/) (2020)
- [Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning](https://mlsanity.com/paper/1909.05477/) (2019)
- [Benchmarking Constraint Inference in Inverse Reinforcement Learning](https://mlsanity.com/paper/2206.09670/) (2022)
- [Maximum Likelihood Constraint Inference from Stochastic Demonstrations](https://mlsanity.com/paper/2102.12554/) (2021)
- [Learning Soft Constraints From Constrained Expert Demonstrations](https://mlsanity.com/paper/2206.01311/) (2022)
- [Learning Parametric Constraints in High Dimensions from Demonstrations](https://mlsanity.com/paper/1910.03477/) (2019)
- [Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations](https://mlsanity.com/paper/2011.04141/) (2020)
- [Making Human-Like Trade-offs in Constrained Environments by Learning from Demonstrations](https://mlsanity.com/paper/2109.11018/) (2021)
- [Maximum Causal Entropy Inverse Constrained Reinforcement Learning](https://mlsanity.com/paper/2305.02857/) (2023)

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges](https://mlsanity.com/paper/2409.07569/) (2024)
- [Consensus Based Stochastic Control](https://mlsanity.com/paper/2501.17801/) (2025)
- [DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems](https://mlsanity.com/paper/2501.18086/) (2025)
- [DRIVE: Dynamic Rule Inference and Verified Evaluation for Constraint-Aware Autonomous Driving](https://mlsanity.com/paper/2508.04066/) (2025)
- [Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion](https://mlsanity.com/paper/2605.31388/) (2026)
- [Learning Unknown Constraints without Unsafe Data via Optimality and Counterfactual Regularization](https://mlsanity.com/paper/2610.09350/) (2026)

## Abstract (arXiv)

In coming up with solutions to real-world problems, humans implicitly adhere to constraints that are too numerous and complex to be specified completely. However, reinforcement learning (RL) agents need these constraints to learn the correct optimal policy in these settings. The field of Inverse Constraint Reinforcement Learning (ICRL) deals with this problem and provides algorithms that aim to estimate the constraints from expert demonstrations collected offline. Practitioners prefer to know a measure of confidence in the estimated constraints, before deciding to use these constraints, which allows them to only use the constraints that satisfy a desired level of confidence. However, prior works do not allow users to provide the desired level of confidence for the inferred constraints. This work provides a principled ICRL method that can take a confidence level with a set of expert demonstrations and outputs a constraint that is at least as constraining as the true underlying constraint with the desired level of confidence. Further, unlike previous methods, this method allows a user to know if the number of expert trajectories is insufficient to learn a constraint with a desired level of confidence, and therefore collect more expert trajectories as required to simultaneously learn constraints with the desired level of confidence and a policy that achieves the desired level of performance.