# Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation

arXiv: https://arxiv.org/abs/1811.06187 · HTML: https://mlsanity.com/paper/1811.06187/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 13 of 13)

- [LazyDAgger: Reducing Context Switching in Interactive Imitation Learning](https://mlsanity.com/paper/2104.00053/) (2021)
- [Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision](https://mlsanity.com/paper/2206.14349/) (2022)
- [REvolve: Reward Evolution with Large Language Models using Human Feedback](https://mlsanity.com/paper/2406.01309/) (2024)
- [Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving](https://mlsanity.com/paper/2109.12516/) (2021)
- [Learning from Active Human Involvement through Proxy Value Propagation](https://mlsanity.com/paper/2502.03369/) (2025)
- [Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving](https://mlsanity.com/paper/2104.07246/) (2021)
- [Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning](https://mlsanity.com/paper/2207.00448/) (2022)
- [Safe and Robust Reinforcement Learning: Principles and Practice](https://mlsanity.com/paper/2403.18539/) (2024)
- [ReIL: A Framework for Reinforced Intervention-based Imitation Learning](https://mlsanity.com/paper/2203.15390/) (2022)
- [Intervention-Assisted Policy Gradient Methods for Online Stochastic Queuing Network Optimization: Technical Report](https://mlsanity.com/paper/2404.04106/) (2024)
- [Safe Navigation for Robotic Digestive Endoscopy via Human Intervention-based Reinforcement Learning](https://mlsanity.com/paper/2409.15688/) (2024)
- [Efficient Soft Actor-Critic with LLM-Based Action-Level Guidance for Continuous Control](https://mlsanity.com/paper/2603.17468/) (2026)
- [Res-HIL: Human-Guided Residual Reinforcement Learning for Sample-Efficient Dexterous Manipulation](https://mlsanity.com/paper/2609.30023/) (2026)

## Abstract (arXiv)

Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety and cost in practical applications. In this paper, we propose the Intervention Aided Reinforcement Learning (IARL) framework, which utilizes human intervened robot-environment interaction to improve the policy. We used the Unmanned Aerial Vehicle (UAV) as the test platform. We built neural networks as our policy to map sensor readings to control signals on the UAV. Our experiment scenarios cover both simulation and reality. We show that our approach substantially reduces the human intervention and improves the performance in autonomous navigation, at the same time it ensures safety and keeps training cost acceptable.