mlsanityData to 14 Oct 2026

Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning

arXiv 2210.16299 ↗First posted 28 Oct 2022eess.SY cs.LG cs.SYcited by 1

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

Abstract (arXiv)

A key challenge in solving the deterministic inverse reinforcement learning (IRL) problem online and in real-time is the existence of multiple solutions. Nonuniqueness necessitates the study of the notion of equivalent solutions, i.e., solutions that result in a different cost functional but same feedback matrix, and convergence to such solutions. While offline algorithms that result in convergence to equivalent solutions have been developed in the literature, online, real-time techniques that address nonuniqueness are not available. In this paper, a regularized history stack observer that converges to approximately equivalent solutions of the IRL problem is developed. Novel data-richness conditions are developed to facilitate the analysis and simulation results are provided to demonstrate the effectiveness of the developed technique.

Key facts. Cited by 1 paper 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 2 of its 47 references that are in the map. First posted on arXiv 28 Oct 2022.

Builds on

2 of 47 references.

Closest ancestors · 1

Same direction, newest first

  1. 2020Online Observer-Based Inverse Reinforcement Learning3 Nov

Other references · 1

  1. 2016First-Person Activity Forecasting with Online Inverse Reinforcement Learningcited by 40

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Pilot Performance modeling via observer-based inverse reinforcement learningcited by 1

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  8. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective