# Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/1806.08479 · HTML: https://mlsanity.com/paper/1806.08479/

## Key facts

- Cited by 3 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 5 of its 16 references that are in the map.
- First posted on arXiv 22 Jun 2018.

## Placed in

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

## Builds on (most cited first, 5 of 5)

- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Repeated Inverse Reinforcement Learning](https://mlsanity.com/paper/1705.05427/) (2017)
- [HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed Rewards](https://mlsanity.com/paper/1604.06508/) (2016)
- [Incorporating Human Domain Knowledge into Large Scale Cost Function Learning](https://mlsanity.com/paper/1612.04318/) (2016)

## Built on by (most cited first, 3 of 3)

- [Active Task-Inference-Guided Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2001.09227/) (2020)
- [Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2401.02160/) (2024)
- [SEAL: SEmantic-Augmented Imitation Learning via Language Model](https://mlsanity.com/paper/2410.02231/) (2024)

## Abstract (arXiv)

Humans are able to understand and perform complex tasks by strategically structuring the tasks into incremental steps or subgoals. For a robot attempting to learn to perform a sequential task with critical subgoal states, such states can provide a natural opportunity for interaction with a human expert. This paper analyzes the benefit of incorporating a notion of subgoals into Inverse Reinforcement Learning (IRL) with a Human-In-The-Loop (HITL) framework. The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states. These subgoal states define a set of subtasks for the learning agent to complete in order to achieve the final goal. The learning agent queries for partial demonstrations corresponding to each subtask as needed when the agent struggles with the subtask. The proposed Human Interactive IRL (HI-IRL) framework is evaluated on several discrete path-planning tasks. We demonstrate that subgoal-based interactive structuring of the learning task results in significantly more efficient learning, requiring only a fraction of the demonstration data needed for learning the underlying reward function with the baseline IRL model.