mlsanityData to 7 Oct 2026

A proof of imitation of Wasserstein inverse reinforcement learning for multi-objective optimization

arXiv 2305.10089 ↗First posted 17 May 2023cs.LG cs.AI stat.MLcited by 3

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

Abstract (arXiv)

We prove Wasserstein inverse reinforcement learning enables the learner's reward values to imitate the expert's reward values in a finite iteration for multi-objective optimizations. Moreover, we prove Wasserstein inverse reinforcement learning enables the learner's optimal solutions to imitate the expert's optimal solutions for multi-objective optimizations with lexicographic order.

Key facts. Cited by 3 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 14 references that are in the map. First posted on arXiv 17 May 2023.

Builds on

2 of 14 references.

References · 2

  1. 2016Generative Adversarial Imitation Learningcited by 1,576
  2. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Exact Solution to Data-Driven Inverse Optimization of MILPs in Finite Time via Gradient-Based Methodscited by 3
  2. 2025Inverse Mixed-Integer Programming: Learning Constraints then Objective Functionscited by 2
  3. 2026Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023A proof of convergence of inverse reinforcement learning for multi-objective optimization
  2. 2019Wasserstein Adversarial Imitation Learning
  3. 2020Regularized Inverse Reinforcement Learning
  4. 2025Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning
  5. 2021Versatile Inverse Reinforcement Learning via Cumulative Rewards
  6. 2025Inverse Reinforcement Learning via Convex Optimization
  7. 2024Pareto Inverse Reinforcement Learning for Diverse Expert Policy Generation
  8. 2022Weighted Maximum Entropy Inverse Reinforcement Learning