# Broad-persistent Advice for Interactive Reinforcement Learning Scenarios

arXiv: https://arxiv.org/abs/2210.05187 · HTML: https://mlsanity.com/paper/2210.05187/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges](https://mlsanity.com/paper/2105.12949/) (2021)
- [A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review](https://mlsanity.com/paper/2007.01544/) (2020)
- [A Comparison of Humanoid Robot Simulators: A Quantitative Approach](https://mlsanity.com/paper/2008.04627/) (2020)
- [Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning](https://mlsanity.com/paper/2009.09575/) (2020)
- [Persistent Rule-based Interactive Reinforcement Learning](https://mlsanity.com/paper/2102.02441/) (2021)

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

- [Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework](https://mlsanity.com/paper/2411.11761/) (2024)

## Abstract (arXiv)

The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Moreover, the information provided by each interaction is not retained and instead discarded by the agent after a single use. In this paper, we present a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Results obtained show that the use of broad-persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer.