mlsanityData to 14 Oct 2026

Scalable Multi-Task Inverse Reinforcement Learning

arXiv 2610.00758 ↗First posted 30 Sep 2026cs.LG stat.MLcited by 0

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

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Abstract (arXiv)

By learning transferable rewards, inverse reinforcement learning (IRL) enables counterfactual evaluation of agents under modified environments. Such transfer places strict requirements on coverage since target environments affect agents' state occupancy. We propose a multi-task IRL method that pools data across multiple agents with different rewards in the same environment under a low-rank assumption. In addition to alleviating coverage requirements, so each task need not visit every state as long as others do, the method offers scalable evaluation of multiple tasks under new environments as computationally intensive planning scales with rank rather than the number of tasks. We provide finite sample guarantees on reward recovery and on policy learning in new environments. Experiments show our method is robust to limited coverage, recovers rewards on and off of each task's support, transfers to target environments at lower regret than baselines, with its computational advantage over per-task methods widening as tasks grow.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 22 of its 35 references that are in the map. First posted on arXiv 30 Sep 2026.

Builds on

22 of 35 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Inverse Reinforcement Learning with Just Classification and a Few Regressions25 Sep
  2. 2024Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms23 Feb
  3. 2023Multi-task Hierarchical Adversarial Inverse Reinforcement Learning22 May
  4. 2021Identifiability in inverse reinforcement learning7 Jun
  5. 2019Meta-Inverse Reinforcement Learning with Probabilistic Context Variables20 Sep

Other references · 17

  1. 2017Deep reinforcement learning from human preferencescited by 3,912
  2. 2016Generative Adversarial Imitation Learningcited by 1,597
  3. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 669
  4. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 490
  5. 2015An Introduction to Matrix Concentration Inequalitiescited by 397
  6. 2016Successor Features for Transfer in Reinforcement Learningcited by 340
  7. 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Taskscited by 327
  8. 2021IQ-Learn: Inverse soft-Q Learning for Imitationcited by 193
  9. 2020On the Theory of Transfer Learning: The Importance of Task Diversitycited by 186
  10. 2016A vector-contraction inequality for Rademacher complexitiescited by 142
  11. 2019RecSim: A Configurable Simulation Platform for Recommender Systemscited by 94
  12. 2024NAVIX: Scaling MiniGrid Environments with JAXcited by 18
5 more
  1. 2016Dynamic Assortment Personalization in High Dimensionscited by 14
  2. 2022Multi-User Reinforcement Learning with Low Rank Rewardscited by 4
  3. 2025Efficient Reward Identification In Max Entropy Reinforcement Learning with Sparsity and Rank Priorscited by 1
  4. 2026BXRL: Behavior-Explainable Reinforcement Learningcited by 1
  5. 2026Provable Multi-Task Reinforcement Learning: A Representation Learning Framework with Low Rank Rewardscited by 1

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Close in wording, no citation either way

  1. 2024Multi Task Inverse Reinforcement Learning for Common Sense Reward
  2. 2026Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
  3. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  4. 2020Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic
  5. 2024How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach
  6. 2026Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning
  7. 2024Inverse Reinforcement Learning with Multiple Planning Horizons
  8. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective