Reinforcement Learning Interpretation Methods: A Survey
Reinforcement Learning (RL) systems achieved outstanding performance in different domains such as Atari games, finance, healthcare, and self-driving cars. However, their black-box nature complicates their use, especially in critical applications such as healthcare. To solve this problem, researchers...
Main Authors: | Alnour Alharin, Thanh-Nam Doan, Mina Sartipi |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9194697/ |
Similar Items
-
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
by: Shijie Jiang, et al.
Published: (2024-07-01) -
Review Study of Interpretation Methods for Future Interpretable Machine Learning
by: Jian-Xun Mi, et al.
Published: (2020-01-01) -
GSIC: A New Interpretable System for Knowledge Exploration and Classification
by: Thanh-Phu Nguyen, et al.
Published: (2020-01-01) -
Evolutionary Learning of Interpretable Decision Trees
by: Leonardo L. Custode, et al.
Published: (2023-01-01) -
Optimal recovery of unsecured debt via interpretable reinforcement learning
by: Michael Mark, et al.
Published: (2022-06-01)