# Opinion-Guided Reinforcement Learning

arXiv: https://arxiv.org/abs/2405.17287 · HTML: https://mlsanity.com/paper/2405.17287/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 66 references that are in the map.
- First posted on arXiv 27 May 2024.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 12 of 12)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Safe RLHF: Safe Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2310.12773/) (2023)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Demonstration-Guided Reinforcement Learning with Learned Skills](https://mlsanity.com/paper/2107.10253/) (2021)
- [Interactive Imitation Learning in Robotics: A Survey](https://mlsanity.com/paper/2211.00600/) (2022)
- [DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback](https://mlsanity.com/paper/1810.11748/) (2018)
- [Managing engineering systems with large state and action spaces through deep reinforcement learning](https://mlsanity.com/paper/1811.02052/) (2018)
- [Reinforcement learning with human advice: a survey](https://mlsanity.com/paper/2005.11016/) (2020)
- [Learning a Low-dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems](https://mlsanity.com/paper/2010.09555/) (2020)
- [Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit Problems](https://mlsanity.com/paper/2008.07386/) (2020)
- [Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information](https://mlsanity.com/paper/2302.10195/) (2023)

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

- [AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture](https://mlsanity.com/paper/2506.06580/) (2025)
- [Complex Model Transformations by Reinforcement Learning with Uncertain Human Guidance](https://mlsanity.com/paper/2506.20883/) (2025)
- [Human-Allied Relational Reinforcement Learning](https://mlsanity.com/paper/2510.16188/) (2025)

## Abstract (arXiv)

Human guidance is often desired in reinforcement learning to improve the performance of the learning agent. However, human insights are often mere opinions and educated guesses rather than well-formulated arguments. While opinions are subject to uncertainty, e.g., due to partial informedness or ignorance about a problem, they also emerge earlier than hard evidence can be produced. Thus, guiding reinforcement learning agents by way of opinions offers the potential for more performant learning processes, but comes with the challenge of modeling and managing opinions in a formal way. In this article, we present a method to guide reinforcement learning agents through opinions. To this end, we provide an end-to-end method to model and manage advisors' opinions. To assess the utility of the approach, we evaluate it with synthetic (oracle) and human advisors, at different levels of uncertainty, and under multiple advice strategies. Our results indicate that opinions, even if uncertain, improve the performance of reinforcement learning agents, resulting in higher rewards, more efficient exploration, and a better reinforced policy. Although we demonstrate our approach through a two-dimensional topological running example, our approach is applicable to complex problems with higher dimensions as well.