mlsanityData to 7 Oct 2026

Reward Shaping for Human Learning via Inverse Reinforcement Learning

arXiv 2002.10904 ↗First posted 25 Feb 2020cs.LG stat.MLcited by 1

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

Abstract (arXiv)

Humans are spectacular reinforcement learners, constantly learning from and adjusting to experience and feedback. Unfortunately, this doesn't necessarily mean humans are fast learners. When tasks are challenging, learning can become unacceptably slow. Fortunately, humans do not have to learn tabula rasa, and learning speed can be greatly increased with learning aids. In this work we validate a new type of learning aid -- reward shaping for humans via inverse reinforcement learning (IRL). The goal of this aid is to increase the speed with which humans can learn good policies for specific tasks. Furthermore this approach compliments alternative machine learning techniques such as safety features that try to prevent individuals from making poor decisions. To achieve our results we first extend a well known IRL algorithm via kernel methods. Afterwards we conduct two human subjects experiments using an online game where players have limited time to learn a good policy. We show with statistical significance that players who receive our learning aid are able to approach desired policies more quickly than the control group.

Key facts. Cited by 1 paper 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 5 of its 29 references that are in the map. First posted on arXiv 25 Feb 2020.

Builds on

5 of 29 references.

Closest ancestors · 1

Same direction, newest first

  1. 2018An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning11 Jun

Other references · 4

  1. 2016Generative Adversarial Imitation Learningcited by 1,576
  2. 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 360
  3. 2016Cooperative Inverse Reinforcement Learningcited by 298
  4. 2016Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translationcited by 12

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Transformable Gaussian Reward Function for Socially-Aware Navigation with Deep Reinforcement Learningcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  2. 2023Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping
  3. 2025Multi-Task Reward Learning from Human Ratings
  4. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  5. 2025Reinforcement Learning from Multi-level and Episodic Human Feedback
  6. 2024Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
  7. 2026Meta-Learned Reward Shaping for Reinforcement Learning from Human Feedback
  8. 2022Inducing Structure in Reward Learning by Learning Features