mlsanityData to 14 Oct 2026

Cost Function Estimation Using Inverse Reinforcement Learning with Minimal Observations

arXiv 2505.08619 ↗First posted 13 May 2025cs.LG cs.ROcited by 2

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

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Abstract (arXiv)

We present an iterative inverse reinforcement learning algorithm to infer optimal cost functions in continuous spaces. Based on a popular maximum entropy criteria, our approach iteratively finds a weight improvement step and proposes a method to find an appropriate step size that ensures learned cost function features remain similar to the demonstrated trajectory features. In contrast to similar approaches, our algorithm can individually tune the effectiveness of each observation for the partition function and does not need a large sample set, enabling faster learning. We generate sample trajectories by solving an optimal control problem instead of random sampling, leading to more informative trajectories. The performance of our method is compared to two state of the art algorithms to demonstrate its benefits in several simulated environments.

Key facts. Cited by 2 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 2 of its 22 references that are in the map. First posted on arXiv 13 May 2025.

Builds on

2 of 22 references.

References · 2

  1. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 406
  2. 2019Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Controlcited by 34

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Learning Human Reaching Optimality Principles from Minimal Observation Inverse Reinforcement Learningcited by 0
  2. 2026Toward Global Intent Inference for Human Motion by Inverse Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2018Inverse reinforcement learning in continuous time and space
  2. 2021A Generalised Inverse Reinforcement Learning Framework
  3. 2020Learning Navigation Costs from Demonstration in Partially Observable Environments
  4. 2019Inverse Dynamic Games Based on Maximum Entropy Inverse Reinforcement Learning
  5. 2022Weighted Maximum Entropy Inverse Reinforcement Learning
  6. 2020Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
  7. 2020Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms
  8. 2025Data-Driven Inverse Optimal Control for Continuous-Time Nonlinear Systems