mlsanityData to 7 Oct 2026

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

arXiv 2407.08910 ↗First posted 12 Jul 2024cs.LG cs.AIcited by 1

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 21 of its 54 references that are in the map. First posted on arXiv 12 Jul 2024.

Builds on

21 of 54 references.

Closest ancestors · 3

Same direction, newest first

  1. 2020f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning2 Oct
  2. 2020Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets19 May
  3. 2019Adversarial Imitation Learning from Incomplete Demonstrations29 May

Other references · 18

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2016Generative Adversarial Imitation Learningcited by 1,576
  3. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
  4. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 748
  5. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  6. 2019Learning by Cheatingcited by 361
  7. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 301
  8. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 123
  9. 2018Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendationcited by 101
  10. 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
  11. 2017A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Unitscited by 50
  12. 2020TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learningcited by 46
6 more
  1. 2019Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Predictioncited by 30
  2. 2020Artificial Intelligence for Social Good: A Surveycited by 29
  3. 2019Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuningcited by 24
  4. 2021Drug Package Recommendation via Interaction-aware Graph Inductioncited by 14
  5. 2019Artificial Intelligence for Social Goodcited by 8
  6. 2023Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Financecited by 7

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimescited by 0

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  3. 2021DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
  4. 2022Planning for Sample Efficient Imitation Learning
  5. 2023DiffAIL: Diffusion Adversarial Imitation Learning
  6. 2024Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation, Transferable Reward Recovery and Algebraic Equilibrium Proof
  7. 2026Accounting for Optimal Control in the Sizing of Isolated Hybrid Renewable Energy Systems Using Imitation Learning
  8. 2020Adversarial Imitation Learning via Random Search