mlsanityData to 7 Oct 2026

Inverse Reinforcement Learning with Missing Data

arXiv 1911.06930 ↗First posted 16 Nov 2019cs.LG cs.AI stat.MLcited by 2

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

Abstract (arXiv)

We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in the demonstrated trajectories. This issue of missing trajectory data or information occurs in many situations, e.g., GPS signals from vehicles moving on a road network are intermittent. In this paper, we propose a tractable approach to directly compute the log-likelihood of demonstrated trajectories with incomplete/missing data. Our algorithm is efficient in handling a large number of missing segments in the demonstrated trajectories, as it performs the training with incomplete data by solving a sequence of systems of linear equations, and the number of such systems to be solved does not depend on the number of missing segments. Empirical evaluation on a real-world dataset shows that our training algorithm outperforms other conventional techniques.

Key facts. Cited by 2 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 2 of its 16 references that are in the map. First posted on arXiv 16 Nov 2019.

Builds on

2 of 16 references.

References · 2

  1. 2016Generative Adversarial Imitation Learningcited by 1,576
  2. 2017Inverse Reinforcement Learning Under Noisy Observationscited by 4

Built on by

Cited by year

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

2 papers.

  1. 2023Reinforcement Learning Approaches for Traffic Signal Control under Missing Datacited by 13
  2. 2021Marginal MAP Estimation for Inverse RL under Occlusion with Observer Noisecited by 3

Similar, not cited

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  1. 2026Quantifying Potential Observation Missingness in Inverse Reinforcement Learning
  2. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  3. 2023Inverse Reinforcement Learning without Reinforcement Learning
  4. 2024Inverse Delayed Reinforcement Learning
  5. 2024In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before An Ongoing Trajectory Terminates
  6. 2023Towards Theoretical Understanding of Inverse Reinforcement Learning
  7. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  8. 2024Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms