mlsanityData to 7 Oct 2026

Lifelong Inverse Reinforcement Learning

arXiv 2207.00461 ↗First posted 1 Jul 2022cs.LGcited by 9

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

Abstract (arXiv)

Methods for learning from demonstration (LfD) have shown success in acquiring behavior policies by imitating a user. However, even for a single task, LfD may require numerous demonstrations. For versatile agents that must learn many tasks via demonstration, this process would substantially burden the user if each task were learned in isolation. To address this challenge, we introduce the novel problem of lifelong learning from demonstration, which allows the agent to continually build upon knowledge learned from previously demonstrated tasks to accelerate the learning of new tasks, reducing the amount of demonstrations required. As one solution to this problem, we propose the first lifelong learning approach to inverse reinforcement learning, which learns consecutive tasks via demonstration, continually transferring knowledge between tasks to improve performance.

Key facts. Cited by 9 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 4 of its 43 references that are in the map. First posted on arXiv 1 Jul 2022.

Builds on

4 of 43 references.

References · 4

  1. 2017One-Shot Imitation Learningcited by 232
  2. 2017One-Shot Visual Imitation Learning via Meta-Learningcited by 225
  3. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
  4. 2016Generalizing Skills with Semi-Supervised Reinforcement Learningcited by 18

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 5.

9 papers.

  1. 2024Online Continual Learning For Interactive Instruction Following Agentscited by 21
  2. 2025A Survey of Continual Reinforcement Learningcited by 16
  3. 2022Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrationscited by 14
  4. 2025Continual Learning for Generative AI: From LLMs to MLLMs and Beyondcited by 9
  5. 2025Hierarchical-Task-Aware Multi-modal Mixture of Incremental LoRA Experts for Embodied Continual Learningcited by 8
  6. 2025Inversely Learning Transferable Rewards via Abstracted Statescited by 1
  7. 2023ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Eventscited by 0
  8. 2024Inverse Reinforcement Learning with Multiple Planning Horizonscited by 0
  9. 2025Supervised Reward Inferencecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2018Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications
  2. 2021Inverse Reinforcement Learning: A Control Lyapunov Approach
  3. 2019Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
  4. 2018Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
  5. 2019Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
  6. 2022Strategy Discovery and Mixture in Lifelong Learning from Heterogeneous Demonstration
  7. 2022Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning
  8. 2021PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning