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

arXiv: https://arxiv.org/abs/2407.08910 · HTML: https://mlsanity.com/paper/2407.08910/

## Key facts

- Cited by 1 papers in the map (data through 14 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.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)
- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 21 of 21)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation](https://mlsanity.com/paper/1807.01473/) (2018)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units](https://mlsanity.com/paper/1704.06300/) (2017)
- [TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2007.14189/) (2020)
- [Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction](https://mlsanity.com/paper/1911.10516/) (2019)
- [Artificial Intelligence for Social Good: A Survey](https://mlsanity.com/paper/2001.01818/) (2020)
- [Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning](https://mlsanity.com/paper/1906.12086/) (2019)
- [f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2010.01207/) (2020)
- [Adversarial Imitation Learning from Incomplete Demonstrations](https://mlsanity.com/paper/1905.12310/) (2019)
- [Drug Package Recommendation via Interaction-aware Graph Induction](https://mlsanity.com/paper/2102.03577/) (2021)
- [Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets](https://mlsanity.com/paper/2005.10622/) (2020)
- [Artificial Intelligence for Social Good](https://mlsanity.com/paper/1901.05406/) (2019)
- [Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance](https://mlsanity.com/paper/2307.03119/) (2023)

## Built on by (most cited first, 1 of 1)

- [SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes](https://mlsanity.com/paper/2506.06649/) (2025)

## 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.