mlsanityData to 7 Oct 2026

Inverse Reinforcement Learning: A Control Lyapunov Approach

arXiv 2104.04483 ↗First posted 9 Apr 2021cs.LG cs.SY eess.SYcited by 5

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

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.

Key facts. Cited by 5 papers in the map (data through 7 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.

Builds on

3 of 18 references.

References · 3

  1. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  2. 2016Safe Learning of Regions of Attraction for Uncertain, Nonlinear Systems with Gaussian Processescited by 38
  3. 2020GP3: A Sampling-based Analysis Framework for Gaussian Processescited by 1

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 1.

5 papers.

  1. 2021Learning the Koopman Eigendecomposition: A Diffeomorphic Approachcited by 9
  2. 2024Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapescited by 2
  3. 2024Automated Feature Selection for Inverse Reinforcement Learningcited by 1
  4. 2022Cross apprenticeship learning framework: Properties and solution approachescited by 0
  5. 2025Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networkscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
  2. 2020Inverse Reinforcement Learning from a Gradient-based Learner
  3. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  4. 2020Inverse Reinforcement Learning via Matching of Optimality Profiles
  5. 2023BC-IRL: Learning Generalizable Reward Functions from Demonstrations
  6. 2023A Novel Variational Lower Bound for Inverse Reinforcement Learning
  7. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  8. 2025Inverse Reinforcement Learning via Convex Optimization