# Inverse Reinforcement Learning: A Control Lyapunov Approach

arXiv: https://arxiv.org/abs/2104.04483 · HTML: https://mlsanity.com/paper/2104.04483/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 3 of 3)

- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Safe Learning of Regions of Attraction for Uncertain, Nonlinear Systems with Gaussian Processes](https://mlsanity.com/paper/1603.04915/) (2016)
- [GP3: A Sampling-based Analysis Framework for Gaussian Processes](https://mlsanity.com/paper/2006.07871/) (2020)

## Built on by (most cited first, 5 of 5)

- [Learning the Koopman Eigendecomposition: A Diffeomorphic Approach](https://mlsanity.com/paper/2110.07786/) (2021)
- [Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapes](https://mlsanity.com/paper/2405.08756/) (2024)
- [Automated Feature Selection for Inverse Reinforcement Learning](https://mlsanity.com/paper/2403.15079/) (2024)
- [Cross apprenticeship learning framework: Properties and solution approaches](https://mlsanity.com/paper/2209.02424/) (2022)
- [Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks](https://mlsanity.com/paper/2504.02607/) (2025)

## Abstract (arXiv)

Inferring the intent of an intelligent agent from demonstrations and subsequently predicting its behavior, is a critical task in many collaborative settings. A common approach to solve this problem is the framework of inverse reinforcement learning (IRL), where the observed agent, e.g., a human demonstrator, is assumed to behave according to an intrinsic cost function that reflects its intent and informs its control actions. In this work, we reformulate the IRL inference problem to learning control Lyapunov functions (CLF) from demonstrations by exploiting the inverse optimality property, which states that every CLF is also a meaningful value function. Moreover, the derived CLF formulation directly guarantees stability of inferred control policies. We show the flexibility of our proposed method by learning from goal-directed movement demonstrations in a continuous environment.