Summary of Pail: Performance Based Adversarial Imitation Learning Engine For Carbon Neutral Optimization, by Yuyang Ye et al.
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization
by Yuyang Ye, Lu-An Tang, Haoyu Wang, Runlong Yu, Wenchao Yu, Erhu He, Haifeng Chen, Hui Xiong
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 a novel method called Performance based Adversarial Imitation Learning (PAIL) to optimize industrial operations for carbon neutrality without relying on pre-defined reward functions. Building upon Deep Reinforcement Learning (DRL) techniques, PAIL employs a Transformer-based policy generator to predict actions and an environmental simulator to update the generated sequences. A discriminator is used to minimize discrepancies between generated and real-world samples, while a Q-learning framework estimates the impact of each action on sustainable development goals (SDG). The paper demonstrates the effectiveness of PAIL in multiple real-world application cases and datasets, outperforming state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us achieve carbon neutrality by using a new way to optimize industrial operations. It uses special learning methods called Deep Reinforcement Learning, which are good at solving complex problems. The method is called PAIL, short for Performance based Adversarial Imitation Learning. It works by predicting what actions will be best and then updating those predictions based on how well they do. The paper shows that this new method works better than other methods in real-world applications. |
Keywords
* Artificial intelligence * Reinforcement learning * Transformer
