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A Framework and Method for Online Inverse Reinforcement Learning

arXiv 1805.07871 ↗First posted 21 May 2018cs.LG cs.AI stat.MLcited by 0

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

Abstract (arXiv)

Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from the observations of its behavior on a task. While this problem has been well investigated, the related problem of em online IRL---where the observations are incrementally accrued, yet the demands of the application often prohibit a full rerun of an IRL method---has received relatively less attention. We introduce the first formal framework for online IRL, called incremental IRL (I2RL), and a new method that advances maximum entropy IRL with hidden variables, to this setting. Our formal analysis shows that the new method has a monotonically improving performance with more demonstration data, as well as probabilistically bounded error, both under full and partial observability. Experiments in a simulated robotic application of penetrating a continuous patrol under occlusion shows the relatively improved performance and speed up of the new method and validates the utility of online IRL.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 1 of its 19 references that are in the map. First posted on arXiv 21 May 2018.

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1 of 19 references.

References · 1

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

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  1. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  2. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  3. 2024Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms
  4. 2018Online inverse reinforcement learning for nonlinear systems
  5. 2021Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
  6. 2023Inverse Reinforcement Learning without Reinforcement Learning
  7. 2021A Generalised Inverse Reinforcement Learning Framework
  8. 2025Inverse Reinforcement Learning via Convex Optimization