mlsanityData to 14 Oct 2026

Inverse Reinforcement Learning with Multiple Planning Horizons

arXiv 2409.18051 ↗First posted 26 Sep 2024cs.LGcited by 0

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

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Abstract (arXiv)

In this work, we study an inverse reinforcement learning (IRL) problem where the experts are planning under a shared reward function but with different, unknown planning horizons. Without the knowledge of discount factors, the reward function has a larger feasible solution set, which makes it harder for existing IRL approaches to identify a reward function. To overcome this challenge, we develop algorithms that can learn a global multi-agent reward function with agent-specific discount factors that reconstruct the expert policies. We characterize the feasible solution space of the reward function and discount factors for both algorithms and demonstrate the generalizability of the learned reward function across multiple domains.

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 12 of its 25 references that are in the map. First posted on arXiv 26 Sep 2024.

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12 of 25 references.

Closest ancestors · 7

Same direction, newest first

  1. 2022Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees4 Oct
  2. 2022Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning22 Sep
  3. 2022Lifelong Inverse Reinforcement Learning1 Jul
  4. 2021Identifiability in inverse reinforcement learning7 Jun
  5. 2019Meta-Inverse Reinforcement Learning with Probabilistic Context Variables20 Sep
  6. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations12 Apr
  7. 2018Multi-task Maximum Entropy Inverse Reinforcement Learning22 May

Other references · 5

  1. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 669
  2. 2020Reinforcement Learning in Economics and Financecited by 58
  3. 2021Challenges for Reinforcement Learning in Healthcarecited by 7
  4. 2019Issues concerning realizability of Blackwell optimal policies in reinforcement learningcited by 2
  5. 2023Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learningcited by 2

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  1. 2025Generalizing Behavior via Inverse Reinforcement Learning with Closed-Form Reward Centroids
  2. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  3. 2020Inverse Reinforcement Learning from a Gradient-based Learner
  4. 2021Versatile Inverse Reinforcement Learning via Cumulative Rewards
  5. 2023Towards Theoretical Understanding of Inverse Reinforcement Learning
  6. 2023BC-IRL: Learning Generalizable Reward Functions from Demonstrations
  7. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  8. 2026Scalable Multi-Task Inverse Reinforcement Learning