# Interaction-limited Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/2007.00425 · HTML: https://mlsanity.com/paper/2007.00425/

## 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 14 of its 38 references that are in the map.
- First posted on arXiv 1 Jul 2020.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 14 of 14)

- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Automated Curriculum Learning for Neural Networks](https://mlsanity.com/paper/1704.03003/) (2017)
- [Reverse Curriculum Generation for Reinforcement Learning](https://mlsanity.com/paper/1707.05300/) (2017)
- [Learning to Execute](https://mlsanity.com/paper/1410.4615/) (2014)
- [An Overview of Machine Teaching](https://mlsanity.com/paper/1801.05927/) (2018)
- [Iterative Machine Teaching](https://mlsanity.com/paper/1705.10470/) (2017)
- [First-Person Activity Forecasting with Online Inverse Reinforcement Learning](https://mlsanity.com/paper/1612.07796/) (2016)
- [Learning Curriculum Policies for Reinforcement Learning](https://mlsanity.com/paper/1812.00285/) (2018)
- [Theory of Curriculum Learning, with Convex Loss Functions](https://mlsanity.com/paper/1812.03472/) (2018)
- [Repeated Inverse Reinforcement Learning](https://mlsanity.com/paper/1705.05427/) (2017)
- [Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications](https://mlsanity.com/paper/1805.07687/) (2018)
- [Interactive Teaching Algorithms for Inverse Reinforcement Learning](https://mlsanity.com/paper/1905.11867/) (2019)

## Abstract (arXiv)

This paper proposes an inverse reinforcement learning (IRL) framework to accelerate learning when the learner-teacher textitinteraction is textitlimited during training. Our setting is motivated by the realistic scenarios where a helpful teacher is not available or when the teacher cannot access the learning dynamics of the student. We present two different training strategies: Curriculum Inverse Reinforcement Learning (CIRL) covering the teacher's perspective, and Self-Paced Inverse Reinforcement Learning (SPIRL) focusing on the learner's perspective. Using experiments in simulations and experiments with a real robot learning a task from a human demonstrator, we show that our training strategies can allow a faster training than a random teacher for CIRL and than a batch learner for SPIRL.