# Active Learning for Risk-Sensitive Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/1909.07843 · HTML: https://mlsanity.com/paper/1909.07843/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 14 Sep 2019.

## Placed in

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

## Builds on (most cited first, 3 of 3)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods](https://mlsanity.com/paper/1711.10055/) (2017)

## Built on by (most cited first, 1 of 1)

- [Eliciting Risk Aversion with Inverse Reinforcement Learning via Interactive Questioning](https://mlsanity.com/paper/2308.08427/) (2023)

## Abstract (arXiv)

One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assumption deviates from actual human behaviors under ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such gap by assuming that humans act according to a random cost with respect to a set of subjectively distorted distributions instead of a fixed one. Such assumption provides the additional flexibility to model human's risk preferences, represented by a risk envelope, in safe-critical tasks. However, like other learning from demonstration techniques, RS-IRL could also suffer inefficient learning due to redundant demonstrations. Inspired by the concept of active learning, this research derives a probabilistic disturbance sampling scheme to enable an RS-IRL agent to query expert support that is likely to expose unrevealed boundaries of the expert's risk envelope. Experimental results confirm that our approach accelerates the convergence of RS-IRL algorithms with lower variance while still guaranteeing unbiased convergence.