# Cycle-of-Learning for Autonomous Systems from Human Interaction

arXiv: https://arxiv.org/abs/1808.09572 · HTML: https://mlsanity.com/paper/1808.09572/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks](https://mlsanity.com/paper/2107.05825/) (2021)
- [Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft](https://mlsanity.com/paper/2112.03482/) (2021)
- [Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning](https://mlsanity.com/paper/2405.09760/) (2024)
- [An Emergency Disposal Decision-making Method with Human--Machine Collaboration](https://mlsanity.com/paper/2305.18165/) (2023)

## Abstract (arXiv)

We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms. We provide a taxonomy to categorize the types of human interaction and present our Cycle-of-Learning framework for autonomous systems that combines different human-interaction modalities with reinforcement learning. Two key concepts provided by our Cycle-of-Learning framework are how it handles the integration of the different human-interaction modalities (demonstration, intervention, and evaluation) and how to define the switching criteria between them.