# Repeated Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/1705.05427 · HTML: https://mlsanity.com/paper/1705.05427/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2017.

## Built on by (most cited first, 29 of 29)

- [Scalable agent alignment via reward modeling: a research direction](https://mlsanity.com/paper/1811.07871/) (2018)
- [On the Expressivity of Markov Reward](https://mlsanity.com/paper/2111.00876/) (2021)
- [Identifiability in inverse reinforcement learning](https://mlsanity.com/paper/2106.03498/) (2021)
- [Invariance in Policy Optimisation and Partial Identifiability in Reward Learning](https://mlsanity.com/paper/2203.07475/) (2022)
- [Interactive Teaching Algorithms for Inverse Reinforcement Learning](https://mlsanity.com/paper/1905.11867/) (2019)
- [How to talk so AI will learn: Instructions, descriptions, and autonomy](https://mlsanity.com/paper/2206.07870/) (2022)
- [Multi-task Maximum Entropy Inverse Reinforcement Learning](https://mlsanity.com/paper/1805.08882/) (2018)
- [Contextual Inverse Optimization: Offline and Online Learning](https://mlsanity.com/paper/2106.14015/) (2021)
- [Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints](https://mlsanity.com/paper/1906.00429/) (2019)
- [Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization](https://mlsanity.com/paper/2011.08541/) (2020)
- [Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning](https://mlsanity.com/paper/2209.10974/) (2022)
- [Learning Shared Safety Constraints from Multi-task Demonstrations](https://mlsanity.com/paper/2309.00711/) (2023)
- [A Primer on Maximum Causal Entropy Inverse Reinforcement Learning](https://mlsanity.com/paper/2203.11409/) (2022)
- [Inverse Reinforcement Learning in Contextual MDPs](https://mlsanity.com/paper/1905.09710/) (2019)
- [Inverse Decision Modeling: Learning Interpretable Representations of Behavior](https://mlsanity.com/paper/2310.18591/) (2023)
- [Curriculum Design for Teaching via Demonstrations: Theory and Applications](https://mlsanity.com/paper/2106.04696/) (2021)
- [Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits](https://mlsanity.com/paper/2106.14866/) (2021)
- [Linguistic communication as (inverse) reward design](https://mlsanity.com/paper/2204.05091/) (2022)
- [Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning](https://mlsanity.com/paper/2406.01793/) (2024)
- [Agency Is Frame-Dependent](https://mlsanity.com/paper/2502.04403/) (2025)
- [Environment Design for Inverse Reinforcement Learning](https://mlsanity.com/paper/2210.14972/) (2022)
- [Inverse Reinforcement Learning with Sub-optimal Experts](https://mlsanity.com/paper/2401.03857/) (2024)
- [Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning](https://mlsanity.com/paper/1806.08479/) (2018)
- [FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functions](https://mlsanity.com/paper/2504.10002/) (2025)
- [Eliciting Risk Aversion with Inverse Reinforcement Learning via Interactive Questioning](https://mlsanity.com/paper/2308.08427/) (2023)
- [Towards Generalized Inverse Reinforcement Learning](https://mlsanity.com/paper/2402.07246/) (2024)
- [The Impact of VR and 2D Interfaces on Human Feedback in Preference-Based Robot Learning](https://mlsanity.com/paper/2503.16500/) (2025)
- [Interaction-limited Inverse Reinforcement Learning](https://mlsanity.com/paper/2007.00425/) (2020)
- [Inference of Utilities and Time Preference in Sequential Decision-Making](https://mlsanity.com/paper/2405.15975/) (2024)

## Abstract (arXiv)

We introduce a novel repeated Inverse Reinforcement Learning problem: the agent has to act on behalf of a human in a sequence of tasks and wishes to minimize the number of tasks that it surprises the human by acting suboptimally with respect to how the human would have acted. Each time the human is surprised, the agent is provided a demonstration of the desired behavior by the human. We formalize this problem, including how the sequence of tasks is chosen, in a few different ways and provide some foundational results.