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PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization

Yuyang Ye, Lu-An Tang, Haoyu Wang, Runlong Yu, Wenchao Yu, Erhu He, Haifeng Chen, Hui Xiong

TL;DR

The paper proposes PAIL, a transformer-based imitation learning engine designed to optimize industrial operations toward carbon neutrality without relying on predefined reward functions. It combines a policy generator, an environment simulator, and a dual policy refinement mechanism comprising a discriminator and a Q-learning based performance estimator to guide action sequences toward higher SDG values. Empirical results on two real-world datasets show that PAIL outperforms multiple baselines and offers interpretable insights into which time steps and actions most influence SDG improvements. The work addresses challenges of historical dependency, complex action spaces, and sequence diversity, delivering a data-driven approach with practical impact for Industry 4.0 sustainability goals.

Abstract

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality.

PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization

TL;DR

The paper proposes PAIL, a transformer-based imitation learning engine designed to optimize industrial operations toward carbon neutrality without relying on predefined reward functions. It combines a policy generator, an environment simulator, and a dual policy refinement mechanism comprising a discriminator and a Q-learning based performance estimator to guide action sequences toward higher SDG values. Empirical results on two real-world datasets show that PAIL outperforms multiple baselines and offers interpretable insights into which time steps and actions most influence SDG improvements. The work addresses challenges of historical dependency, complex action spaces, and sequence diversity, delivering a data-driven approach with practical impact for Industry 4.0 sustainability goals.

Abstract

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality.
Paper Structure (26 sections, 19 equations, 9 figures, 1 table)

This paper contains 26 sections, 19 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminary
  3. Problem Definition
  4. Imitation Learning
  5. Methodology
  6. Policy Generator
  7. Environment Simulator
  8. Policy Optimization
  9. Discriminator of PAIL
  10. Performance Estimator
  11. Experiments
  12. Experimental Settings
  13. Dataset
  14. Metrics
  15. Baseline Methods
  16. ...and 11 more sections

Figures (9)

  • Figure 1: The PAIL framework for carbon neutral optimization.
  • Figure 2: The framework of policy generator.
  • Figure 3: The Overall performance of baselines and PAIL for the oil production dataset.
  • Figure 4: The Overall performance of baselines and PAIL for the supply chain dataset.
  • Figure 5: Performance comparison of PAIL and variants.
  • ...and 4 more figures

Theorems & Definitions (1)

  • Definition 1: Problem Definition