What is it about?
This paper stresses the importance of explanations needed for black-boxed reinforcement learning models in energy systems. Energy systems are critical systems; hence, explanations for these models are of key importance. In this paper, we have examined how reinforcement learning techniques can solve optimization problems in energy systems with the proper explanations of the results so they are understandable by laymen.
Featured Image
Photo by Chelsea on Unsplash
Why is it important?
We have observed that reinforcement learning is quite popular in energy systems. However, when it comes to explaining these black-boxed models, there is a gap, especially in the energy domain. In this paper, we have stressed how state-of-the-art algorithms like Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) can be explained in terms of variable importance and other explanatory methods when these algorithms are used to solve electricity pricing optimization problems.
Perspectives
I hope this article will help in bridging the gap between XAI and energy systems when RL algorithms are applied. I feel that this is a thought-provoking article.
Hallah Butt
Karlsruher Institut fur Technologie
Read the Original
This page is a summary of: Why Reinforcement Learning in Energy Systems Needs Explanations, April 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3648505.3648510.
You can read the full text:
Contributors
The following have contributed to this page
