Summary of A Unified Uncertainty-aware Exploration: Combining Epistemic and Aleatory Uncertainty, by Parvin Malekzadeh et al.
A unified uncertainty-aware exploration: Combining epistemic and aleatory uncertainty
by Parvin Malekzadeh, Ming Hou, Konstantinos N. Plataniotis
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an algorithm for reinforcement learning that addresses the challenge of exploration by incorporating both aleatory and epistemic uncertainty into decision-making. The existing methods estimate these uncertainties separately and combine them additively, which can lead to excessive risk-taking behavior. In contrast, this new approach unifies the estimation of aleatory and epistemic uncertainty and quantifies their combined effect for a risk-sensitive exploration. The proposed method builds on a novel extension of distributional reinforcement learning that models a parameterized return distribution with random variables representing epistemic uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Exploration is a big challenge in artificial intelligence, and this paper helps to solve it by combining two types of uncertainty: aleatory and epistemic. Right now, most algorithms separate these uncertainties and add them together, but that can make the system take too many risks. This new method unites the estimation of both uncertainties and takes into account how they work together for a more cautious approach. The idea is inspired by a new way to use reinforcement learning that models different possibilities for the outcome. |
Keywords
* Artificial intelligence * Reinforcement learning
