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# Papers
Papers related to machine learning, deep learning, and reinforcement learning.

A good way to track the paper reading may be through Google Docs?
- LLMs/FMs: [Google doc](https://docs.google.com/document/d/10LyjAvKb8kFaFYMijqM5pFcoKeOakiRQxLG0rXWHhtg/edit?usp=sharing) [Lastly updated: 1/6/2026]
- Computer Vision: [Google doc](https://docs.google.com/document/d/1hPYpAL-tMsVJr_kpHm9ZoqYlXL7HNgKPENKJW9A1NzU/edit?usp=sharing) [Lastly updated: 1/6/2026]
- (Multi-agent) Reinforcement Learning: [Google doc](https://docs.google.com/document/d/1dogKHumtd4jlL8_Pcptat8ExM9TpRZDe3xQlxwmA9xQ/edit?usp=sharing) [Lastly updated: 4/19/2025]
- Autonomous Driving: [Google doc](https://docs.google.com/document/d/1sWfj3T-_d5-Q_Ud_WB9ouDPI8zrBO_d3cpuQx1LWzrg/edit?usp=sharing) [Lastly updated: 10/06/2024]
- Smart Agriculture: [Google doc](https://docs.google.com/document/d/1Af4HnNgPOLogEsSMNJCZFtwT_ogHgQ_Jjj_nPe_8QWs/edit?usp=sharing) [Lastly updated: 1/6/2026]
- Robotics: [Google doc](https://docs.google.com/document/d/1qibziX1U-n64Bg-9TGc8i8hfYX5toeumTMfZNnTdquw/edit?usp=sharing) [Lastly updated: 1/6/2026]
- Power Systems: [Google doc](https://docs.google.com/document/d/16jDfJid8J3ICNhswkaWYIVFpH70KaVBnNeGRQF4AkbU/edit?usp=sharing) [Lastly updated: 10/06/2024]
- Time-series Models: [Google doc](https://docs.google.com/document/d/1EF6r84Xd82ohaqNysZK8Q1Ff6TTXGT87XX5KW-yeuzs/edit?usp=sharing) [Lastly updated: 10/06/2024]
- Good Tutorials: [Google doc](https://docs.google.com/document/d/1oAS0gmIb5XE-6HROiC-pua3zXn5pA1dg8ecpUSXguA4/edit?usp=sharing) [Lastly updated: 1/6/2026]
- General Research Ideas: [Google doc](https://docs.google.com/document/d/1v7LCsCPqzpOu4p8ycwkVBMUbrzWGYR-g/edit?usp=sharing&ouid=116146847534767871385&rtpof=true&sd=true) [Lastly updated: 10/06/2024]

# Fresh Papers
- Li, Yuecheng, Hongwen He, Amir Khajepour, Yong Chen, Weiwei Huo, and Hao Wang. **"Deep reinforcement learning for intelligent energy management systems of hybrid-electric powertrains: Recent advances, open issues, and prospects."** IEEE Transactions on Transportation Electrification (2024).
- Shi, Zhonghao, Ellen Landrum, Amy O. Connell, Mina Kian, Leticia Pinto-Alva, Kaleen Shrestha, Xiaoyuan Zhu, and Maja J. Matarić. **"How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction: A Brief Survey."** arXiv preprint arXiv:2404.00938 (2024).
- Zhuang, Weiming, Chen Chen, and Lingjuan Lyu. **"When foundation model meets federated learning: Motivations, challenges, and future directions."** arXiv preprint arXiv:2306.15546 (2023).
- Li, Xinran, and Jun Zhang. **"Context-aware Communication for Multi-agent Reinforcement Learning."** arXiv preprint arXiv:2312.15600 (2023).
- Gao, Tianyu, Xingcheng Yao, and Danqi Chen. **"Simcse: Simple contrastive learning of sentence embeddings."** arXiv preprint arXiv:2104.08821 (2021).
- Guan, Cong, et al. **"Efficient Multi-agent Communication via Self-supervised Information Aggregation."** Advances in Neural Information Processing Systems 35 (2022): 1020-1033.
- Yuan, William, et al. **"Transformer in Reinforcement Learning for Decision-Making: A Survey."** (2023).
- He, Sihong, et al. **"Data-driven distributionally robust electric vehicle balancing for autonomous mobility-on-demand systems under demand and supply uncertainties."** IEEE Transactions on Intelligent Transportation Systems (2023).
- Chafii, Marwa, et al. **"Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks."** arXiv preprint arXiv:2309.06021 (2023).
- Faghri, Fartash, et al. **"Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement."** arXiv preprint arXiv:2303.08983 (2023).
- Wang, Letian, et al. **"Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors."** arXiv preprint arXiv:2305.04412 (2023).
- Jiang, Qingsong, et al. **"Deep-reinforcement-learning-based water diversion strategy."** Environmental Science and Ecotechnology (2023): 100298.
- Prudencio, Rafael Figueiredo, Marcos ROA Maximo, and Esther Luna Colombini. **"A survey on offline reinforcement learning: Taxonomy, review, and open problems."** IEEE Transactions on Neural Networks and Learning Systems (2023).
- Chen, Wubing. **"Learning Multi-intersection Traffic Signal Control via Coevolutionary Multi-Agent Reinforcement Learning."** (2023).
- Chen, Xianda, et al. **"FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling."** arXiv preprint arXiv:2306.05381 (2023).
- Yadavalli, Sushma Reddy, Lokesh Chandra Das, and Myounggyu Won. **"RLPG: Reinforcement Learning Approach for Dynamic Intra-Platoon Gap Adaptation for Highway On-Ramp Merging."** arXiv preprint arXiv:2212.03497 (2022).

## Contents
* [Large Language Models](#large-language-models)
  * [LLM Survey](#llm-survey)
    
* [Reinforcement Learning](#reinforcement-Learning)
  * [Survey](#survey)
  * [Value-based](#value-based)
  * [Policy-based](#policy-based)
  * [Model Based](#model-based)
  * [Offline RL](#offline-rl)
  * [Imitation Learning](#imitation-learning)
  * [Semi-supervised](#semi-supervised)
  * [Reward Shaping](#reward-shaping)
  * [Inverse Reinforcement Learning](#inverse-reinforcement-learning)
  * [Transfer Learning](#transfer-learning)
  * [Diffusion Models RL](#diffusion-models-rl)
  * [Foundation Models in RL](#foundation-models-in-rl)
  * [Transformer](#transformer)
  * [Federated Reinforcement Learning](#federated-reinforcement-learning)
  * [Constrained Policy Optimization](#constrained-policy-optimization)
  * [Control](#control)
  * [Applications](#applications)
    * [Intelligent Transportation Systems](#intelligent-transportation-systems)
    * [Gaming](#gaming)
    * [Robotics](#robotics)
    * [Quantum Control](#quantum-control)
    * [UAVs](#uavs)
    * [Environment](#environment)

* [Multi Agent Reinforcement Learning](#multi-agent-reinforcement-learning)
  * [Survey](#survey)
  * [Value-based MARL](#value-based-marl)
  * [Policy-based MARL](#policy-based-marl)
  * [Communication](#communication)
  * [Parameter Sharing](#parameter-sharing)
  * [Constrained Policy Optimisation MARL](#constrained-policy-optimisation-marl)
  * [Graph Convolutional Reinforcement Learning](#graph-convolutional-reinforcement-learning)
  * [Offline MARL](#offline-marl)
  * [Attention](#attention)
  * [Multi-agent Imitation Learning](#multi-agent-imitation-learning)
  * [Simulator](#simulator)
  * [Traffic Applications](#traffic-applications)
    * [Autonomous Driving](#autonomous-driving)
    * [Traffic Signal Control](#traffic-signal-control)

* [Machine Learning](#machine-learning)
  * [Multiple Instance Learning](#multiple-instance-learning)
  * [Continual Learning](#continual-learning)
  * [Privacy](#privacy)
  * [Large Language Models](#large-language-models)

* [Computer Vision](computer-vision)
  * [Foundation Models](#foundation-models)
  * [Semi Supervised Learning](#semi-supervised-learning)
  * [Image Classification](#image-classification)
  * [Object Detection](#object-detection)
  * [Image Segmentation](#image-segmentation)
  * [Panoptic Segmentation](#panoptic-segmentation)
  * [GANs](#gans)
  * [Diffusion Models](#diffusion-models)
  * [Image Synthsis](#image-synthsis)
  * [Transformers](#transformers)
  * [Domain Adaptation](#domain-adaptation)
  * [Others](#others)

* [Meta Learning](#meta-Learning)
  * [Meta Learning](#meta-learning)
  * [Meta MARL](#meta-marl)
  * [Offline Meta](#offline-meta)
  * [Traffic Applications](traffic-applications)
  
* [Power System](#power-system)
  * [Smart Buildings](#smart-buildings) 
  * [Voltage and Frequency Control](#voltage-and-frequency-control)
  * [Energy Trading](#energy-trading)
  * [Testbed](#testbed)
  * [Load Control](#load-control)
  
* [Precision Agriculture](#precision-agriculture)
  * [Weed Control](#weed-control)
  * [Fruit Detection](#fruit-detection)
  * [Data Augmentation](#data-augmentation)
  * [Meat Science](#meat-science)
  * [Agricultural Robots](#agricultural-robots)
  * [Plant Phenotyping](#plant-phenotyping)
  * [GANs in Agriculture](#gans-in-agriculture)
  * [Label-efficient Learning in Agriculture](#label-efficient-learning-in-agriculture)
  
* [Robotics](#robotics)
  * [Soft Robots](#soft-robots)
  
* [Tricks](#tricks)
  * [Ensemble](#ensemble)
  * [Curriculum Learning](#curriculum-learning)
  * [Contrastive Learning](#contrastive-learning)
  * [Auxiliary Task](#auxiliary-task)
  * [Noise](#noise)
  
* [Impressive Works](#impressive-works)
  * [Multi-agent Systems](#multi-agent-systems)
  * [Smart Agriculture](#smart-agriculture)
  * [Reinforcement Learning](#reinforcement-learning)

# Large Language Models

## LLM Survey
- Shi, Zhonghao, Ellen Landrum, Amy O. Connell, Mina Kian, Leticia Pinto-Alva, Kaleen Shrestha, Xiaoyuan Zhu, and Maja J. Matarić. "How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction: A Brief Survey." arXiv preprint arXiv:2404.00938 (2024).

# Reinforcement Learning
## Survey

## Value-based
- [HER] Andrychowicz, Marcin, et al. **"Hindsight experience replay."** Advances in neural information processing systems 30 (2017).
- Yang, Zhihan, and Hai Nguyen. **"Recurrent off-policy baselines for memory-based continuous control."** arXiv preprint arXiv:2110.12628 (2021).
- [DRQN]: Hausknecht, Matthew, and Peter Stone. **"Deep recurrent q-learning for partially observable mdps."** arXiv preprint arXiv:1507.06527 (2015).
- [Esemble] Lan, Qingfeng, et al. **"Maxmin q-learning: Controlling the estimation bias of q-learning."** arXiv preprint arXiv:2002.06487 (2020).
- [Esemble] Chen, Xinyue, et al. **"Randomized ensembled double q-learning: Learning fast without a model."** arXiv preprint arXiv:2101.05982 (2021).
- [Esemble] Hiraoka, Takuya, et al. **"Dropout Q-Functions for Doubly Efficient Reinforcement Learning."** arXiv preprint arXiv:2110.02034 (2021).

## Policy-based
- Xu, Mengda, Manuela Veloso, and Shuran Song. **"ASPiRe: Adaptive Skill Priors for Reinforcement Learning."** arXiv preprint arXiv:2209.15205 (2022).
- (Auxiliary tasks) Jaderberg, Max, et al. **"Reinforcement learning with unsupervised auxiliary tasks."** arXiv preprint arXiv:1611.05397 (2016).

## Offline RL
- [Survey]: Prudencio, Rafael Figueiredo, Marcos ROA Maximo, and Esther Luna Colombini. **"A survey on offline reinforcement learning: Taxonomy, review, and open problems."** IEEE Transactions on Neural Networks and Learning Systems (2023).
- [Survey]: Levine, Sergey, et al. **"Offline reinforcement learning: Tutorial, review, and perspectives on open problems."** arXiv preprint arXiv:2005.01643 (2020).

- (BCQ): Fujimoto, Scott, David Meger, and Doina Precup. **"Off-policy deep reinforcement learning without exploration."** International Conference on Machine Learning. PMLR, 2019.
- (BEAR) Kumar, Aviral, et al. **"Stabilizing off-policy q-learning via bootstrapping error reduction."** arXiv preprint arXiv:1906.00949 (2019).
- Chen, Lili, et al. **"Decision transformer: Reinforcement learning via sequence modeling."** arXiv preprint arXiv:2106.01345 (2021).
- Janner, Michael, Qiyang Li, and Sergey Levine. **"Reinforcement Learning as One Big Sequence Modeling Problem."** arXiv preprint arXiv:2106.02039 (2021).
- Fujimoto, Scott, and Shixiang Shane Gu. **"A Minimalist Approach to Offline Reinforcement Learning."** arXiv preprint arXiv:2106.06860 (2021).
- Mandlekar, Ajay, et al. **"Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data."**
  2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020

**Offline-to-Online**
- Nair, Ashvin, et al. **"AWAC: Accelerating Online Reinforcement Learning with Offline Datasets."** (2020).
- Lee, Seunghyun, et al. **"Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble."** arXiv preprint arXiv:2107.00591 (2021).

## Model Based
- Kurutach, Thanard, et al. **"Model-ensemble trust-region policy optimization."** arXiv preprint arXiv:1802.10592 (2018).
- Matsushima, Tatsuya, et al. **"Deployment-efficient reinforcement learning via model-based offline optimization."** arXiv preprint arXiv:2006.03647 (2020).
- Zhang, Marvin, et al. **"Solar: Deep structured representations for model-based reinforcement learning."** International Conference on Machine Learning. PMLR, 2019.
- Kaiser, Lukasz, et al. **"Model-based reinforcement learning for atari."** arXiv preprint arXiv:1903.00374 (2019).

**Uncertainty Estimate**
- Yu, Tianhe, et al. **"Mopo: Model-based offline policy optimization."** arXiv preprint arXiv:2005.13239 (2020).
- (LOMPO) Rafailov, Rafael, et al. **"Offline reinforcement learning from images with latent space models."** Learning for Dynamics and Control. PMLR, 2021.

## Imitation Learning
- Wang, Letian, et al. **"Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors."** arXiv preprint arXiv:2305.04412 (2023).
- Chen, Dian, et al. **"Learning by cheating." Conference on Robot Learning.** PMLR, 2020.
- Lynch, Corey, et al. **"Learning latent plans from play."** Conference on Robot Learning. PMLR, 2020.
- (BCQ) Torabi, Faraz, Garrett Warnell, and Peter Stone. **"Behavioral cloning from observation."** arXiv preprint arXiv:1805.01954 (2018).
- (ILPO) Edwards, Ashley, et al. **"Imitating latent policies from observation."** International Conference on Machine Learning. PMLR, 2019.

## Semi-supervised
- Park, Jongjin, et al. **"SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning."** arXiv preprint arXiv:2203.10050 (2022).
- Finn, Chelsea, et al. **"Generalizing skills with semi-supervised reinforcement learning."** arXiv preprint arXiv:1612.00429 (2016).

## Hierarchical Reinforcement Learning
- Nachum, Ofir, et al. **"Data-efficient hierarchical reinforcement learning."** arXiv preprint arXiv:1805.08296 (2018).

## Reward Shaping
- Ng, Andrew Y., Daishi Harada, and Stuart Russell. **"Policy invariance under reward transformations: Theory and application to reward shaping."** Icml. Vol. 99. 1999.

## Inverse Reinforcement Learning
- (FORM) Jaegle, Andrew, et al. **"Imitation by Predicting Observations."** International Conference on Machine Learning. PMLR, 2021.
- Christiano, Paul F., et al. **"Deep reinforcement learning from human preferences."** Advances in neural information processing systems 30 (2017).

## Transfer Learning
- Cang, Catherine, et al. **"Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL."** arXiv preprint arXiv:2106.09119 (2021).

## Diffusion Models RL
- Wang, Zhendong, Jonathan J. Hunt, and Mingyuan Zhou. **"Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning."** arXiv preprint arXiv:2208.06193 (2022).
- Janner, Michael, et al. **"Planning with Diffusion for Flexible Behavior Synthesis."** arXiv preprint arXiv:2205.09991 (2022).

## Foundation Models in RL
- Di Palo, Norman, et al. **"Towards A Unified Agent with Foundation Models."** Workshop on Reincarnating Reinforcement Learning at ICLR 2023. 2023.

## Hybrid Action Space
- Li, Boyan, et al. **"Hyar: Addressing discrete-continuous action reinforcement learning via hybrid action representation."** arXiv preprint arXiv:2109.05490 (2021).
- Neunert, Michael, et al. **"Continuous-discrete reinforcement learning for hybrid control in robotics."** Conference on Robot Learning. PMLR, 2020.

## Transformer
- Mao, Hangyu, et al. **"Transformer in Transformer as Backbone for Deep Reinforcement Learning."** arXiv preprint arXiv:2212.14538 (2022).

## Federated Reinforcement Learning
- [Survey] Beltrán, Enrique Tomás Martínez, et al. **"Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges."** arXiv preprint arXiv:2211.08413 (2022).
- [Survey] Qi, Jiaju, et al. **"Federated reinforcement learning: Techniques, applications, and open challenges."** arXiv preprint arXiv:2108.11887 (2021).

## Constrained Policy Optimization
- Achiam, Joshua, et al. **"Constrained policy optimization." International conference on machine learning.** PMLR, 2017.

## Control
- Amos, Brandon, et al. **"Differentiable mpc for end-to-end planning and control."** Advances in neural information processing systems 31 (2018).

## Applications

### Intelligent Transportation Systems
- - Chen, Xianda, et al. **"FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling."** arXiv preprint arXiv:2306.05381 (2023).
- Yadavalli, Sushma Reddy, Lokesh Chandra Das, and Myounggyu Won. **"RLPG: Reinforcement Learning Approach for Dynamic Intra-Platoon Gap Adaptation for Highway On-Ramp Merging."** arXiv preprint arXiv:2212.03497 (2022).
- Cao, Zhong, et al. **"Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning."** Nature Machine Intelligence 5.2 (2023): 145-158.
- Huang, Wenhui, et al. **"Goal-guided Transformer-enabled Reinforcement Learning for Efficient Autonomous Navigation."** arXiv preprint arXiv:2301.00362 (2023).
- Liu, Haochen, et al. **"Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving."** arXiv preprint arXiv:2208.12263 (2022).
- Mavrogiannis, Angelos, Rohan Chandra, and Dinesh Manocha.  **"B-GAP: Behavior-Guided Action Prediction for Autonomous Navigation."** arXiv preprint arXiv:2011.03748 (2020).

### Gaming
- Zha, Daochen, et al. **"DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning."** arXiv preprint arXiv:2106.06135 (2021).

### Robotics
- Haarnoja, Tuomas, et al. **"Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning."** arXiv preprint arXiv:2304.13653 (2023).
- Evans, Benjamin, et al. **"Accelerating Online Reinforcement Learning via Supervisory Safety Systems."** arXiv preprint arXiv:2209.11082 (2022).
- Lee, Joonho, et al. **"Learning quadrupedal locomotion over challenging terrain."** Science robotics 5.47 (2020): eabc5986.

### Environment
- Jiang, Qingsong, et al. **"Deep-reinforcement-learning-based water diversion strategy."** Environmental Science and Ecotechnology (2023): 100298.

### Quantum Control
- Ma, Hailan, et al. **"Curriculum-based deep reinforcement learning for quantum control."** IEEE Transactions on Neural Networks and Learning Systems (2022).

### UAVs
- Joshi, Bhaskar, Dhruv Kapur, and Harikumar Kandath. **"Sim-to-Real Deep Reinforcement Learning based Obstacle Avoidance for UAVs under Measurement Uncertainty."** arXiv preprint arXiv:2303.07243 (2023).

# Multi Agent Reinforcement Learning

## Survey MARL
- Yuan, William, et al. **"Transformer in Reinforcement Learning for Decision-Making: A Survey."** (2023).
- Da Silva, Felipe Leno, and Anna Helena Reali Costa. **"A survey on transfer learning for multiagent reinforcement learning systems."** Journal of Artificial Intelligence Research 64 (2019): 645-703.
- Wong, Annie, et al. **"Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches."** arXiv preprint arXiv:2106.15691 (2021).

## Value-based MARL
- Guan, Cong, et al. **"Efficient Multi-agent Communication via Self-supervised Information Aggregation."** Advances in Neural Information Processing Systems 35 (2022): 1020-1033.
- VDN (2017): Sunehag, Peter, et al. **"Value-decomposition networks for cooperative multi-agent learning."** arXiv preprint arXiv:1706.05296 (2017).
- QMIX (2018): Rashid, Tabish, et al. **"QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning."** arXiv preprint arXiv:1803.11485 (2018).
- DIAL (2016): Foerster, Jakob, et al. **"Learning to communicate with deep multi-agent reinforcement learning."** Advances in neural information processing systems. 2016.
- CommNet (2016): Sukhbaatar, Sainbayar, and Rob Fergus. **"Learning multiagent communication with backpropagation."** Advances in neural information processing systems. 2016.
- IAC (2021): Ma, Xiaoteng, et al. **"Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning."** arXiv preprint arXiv:2102.06042 (2021).

## Policy-based MARL
- Wen, Muning, et al. **"Multi-Agent Reinforcement Learning is a Sequence Modeling Problem."** arXiv preprint arXiv:2205.14953 (2022).
- Yu, Chao, et al. **"The surprising effectiveness of ppo in cooperative, multi-agent games."** arXiv preprint arXiv:2103.01955 (2021).
- Kuba, Jakub Grudzien, et al. **"Trust region policy optimisation in multi-agent reinforcement learning."** arXiv preprint arXiv:2109.11251 (2021).
- Kuba, Jakub Grudzien, et al. **"Settling the variance of multi-agent policy gradients."** Advances in Neural Information Processing Systems 34 (2021): 13458-13470.
- ConsensusNet (2018): Zhang, Kaiqing, et al. **"Fully decentralized multi-agent reinforcement learning with networked agents."** arXiv preprint arXiv:1802.08757 (2018).
- MAAC: Iqbal, Shariq, and Fei Sha. **"Actor-attention-critic for multi-agent reinforcement learning."** International Conference on Machine Learning. PMLR, 2019.
- NeurComm: Chu, Tianshu, Sandeep Chinchali, and Sachin Katti. **"Multi-agent Reinforcement Learning for Networked System Control."** arXiv preprint arXiv:2004.01339 (2020).

## Communication
- Li, Xinran, and Jun Zhang. **"Context-aware Communication for Multi-agent Reinforcement Learning."** arXiv preprint arXiv:2312.15600 (2023).
- Chafii, Marwa, et al. **"Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks."** arXiv preprint arXiv:2309.06021 (2023).
- Zhu, Changxi, Mehdi Dastani, and Shihan Wang. **"A survey of multi-agent reinforcement learning with communication."** arXiv preprint arXiv:2203.08975 (2022).
- [MASIA] Guan, Cong, et al. **"Efficient Multi-agent Communication via Self-supervised Information Aggregation."** Advances in Neural Information Processing Systems 35 (2022): 1020-1033.
- Kim, Woojun, Jongeui Park, and Youngchul Sung. **"Communication in multi-agent reinforcement learning: Intention sharing."** International Conference on Learning Representations. 2021.
- [NeurComm] Chu, Tianshu, Sandeep Chinchali, and Sachin Katti. **"Multi-agent reinforcement learning for networked system control."** arXiv preprint arXiv:2004.01339 (2020).
- [IC3Net] Singh, Amanpreet, Tushar Jain, and Sainbayar Sukhbaatar. "Learning when to communicate at scale in multiagent cooperative and competitive tasks." arXiv preprint arXiv:1812.09755 (2018).
- [COMA] Foerster, Jakob, et al. **"Counterfactual multi-agent policy gradients."** Proceedings of the AAAI conference on artificial intelligence. Vol. 32. No. 1. 2018.
- [NMARL] Zhang, Kaiqing, et al. **"Fully decentralized multi-agent reinforcement learning with networked agents."** International Conference on Machine Learning. PMLR, 2018.
- [DIAL] Foerster, Jakob, et al. "Learning to communicate with deep multi-agent reinforcement learning." Advances in neural information processing systems 29 (2016).
- [CommNet] Sukhbaatar, Sainbayar, and Rob Fergus. **"Learning multiagent communication with backpropagation."** Advances in neural information processing systems 29 (2016).
 
 
## Parameter Sharing
- Gupta, Jayesh K., Maxim Egorov, and Mykel Kochenderfer. **"Cooperative multi-agent control using deep reinforcement learning." International Conference on Autonomous Agents and Multiagent Systems**. Springer, Cham, 2017.
- Lin, Kaixiang, et al. **"Efficient large-scale fleet management via multi-agent deep reinforcement learning."** Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018.

## Constrained Policy Optimisation MARL
- Gu, Shangding, et al. **"Multi-agent constrained policy optimisation."** arXiv preprint arXiv:2110.02793 (2021).

## Graph Convolutional Reinforcement Learning
- Kortvelesy, Ryan, Steven Morad, and Amanda Prorok. **"Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning."** arXiv preprint arXiv:2302.12826 (2023).
- Jiang, Jiechuan, et al. **"Graph convolutional reinforcement learning."** arXiv preprint arXiv:1810.09202 (2018).
- Dong, Jiqian, et al. **"A DRL-based Multiagent Cooperative Control Framework for CAV Networks: a Graphic Convolution Q Network."** arXiv preprint arXiv:2010.05437 (2020).

## Offline MARL
- Pan, Ling, et al. **"Plan better amid conservatism: Offline multi-agent reinforcement learning with actor rectification."** International Conference on Machine Learning. PMLR, 2022.
- Yang, Yiqin, et al. "Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning." Advances in Neural Information Processing Systems 34 (2021): 10299-10312.

## Attention
- Guo, Xudong, Daming Shi, and Wenhui Fan. **"Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism."** arXiv preprint arXiv:2301.01919 (2023).
- Qi, Shuhan, et al. **"Cascaded Attention: Adaptive and Gated Graph Attention Network for Multiagent Reinforcement Learning."** IEEE Transactions on Neural Networks and Learning Systems (2022).

## Multi-agent Imitation Learning
- Wang, Hongwei, et al. **"Multi-Agent Imitation Learning with Copulas."** arXiv preprint arXiv:2107.04750 (2021).

## Simulator
- Peng, Bei, et al. **"Facmac: Factored multi-agent centralised policy gradients."** Advances in Neural Information Processing Systems 34 (2021): 12208-12221.

## Traffic Applications

### Autonomous Driving
- Li, Meng, et al. **"Enhancing Cooperation of Vehicle Merging Control in Heavy Traffic Using Communication-Based Soft Actor-Critic Algorithm."** IEEE Transactions on Intelligent Transportation Systems (2022).
- Zhang, Jiawei, et al. **"Multi-Agent DRL-Based Lane Change With Right-of-Way Collaboration Awareness."** IEEE Transactions on Intelligent Transportation Systems (2022).
- self-play: Tang, Yichuan. **"Towards learning multi-agent negotiations via self-play."** Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops. 2019.

### Traffic Signal Control

# Machine Learning

## Multiple Instance Learning
- Amores, Jaume. **"Multiple instance classification: Review, taxonomy and comparative study."** Artificial intelligence 201 (2013): 81-105.

## Continual Learning
- Wang, Liyuan, et al. **"A Comprehensive Survey of Continual Learning: Theory, Method and Application."** arXiv preprint arXiv:2302.00487 (2023).

## Privacy
- Abadi, Martin, et al. **"Deep learning with differential privacy."** Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 2016.

## Large Language Models
- Bai, Yuntao, et al. **"Constitutional AI: Harmlessness from AI Feedback."** arXiv preprint arXiv:2212.08073 (2022).
- Huang, Shaohan, et al. **"Language is not all you need: Aligning perception with language models."** arXiv preprint arXiv:2302.14045 (2023).

# Computer Vision
## Foundation Models
- Zhuang, Weiming, Chen Chen, and Lingjuan Lyu. **"When foundation model meets federated learning: Motivations, challenges, and future directions."** arXiv preprint arXiv:2306.15546 (2023).
- Moor, Michael, et al. **"Foundation models for generalist medical artificial intelligence."** Nature 616.7956 (2023): 259-265.

## Semi Supervised Learning
- Zhang, Jifan, et al. **"LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning."** arXiv preprint arXiv:2306.09910 (2023).
- Chen, Hao, et al. **"SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning."** arXiv preprint arXiv:2301.10921 (2023).
- Wang, Yidong, et al. **"Freematch: Self-adaptive thresholding for semi-supervised learning."** arXiv preprint arXiv:2205.07246 (2022).
- Zhang, Bowen, et al. **"Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling."** Advances in Neural Information Processing Systems 34 (2021): 18408-18419.
- Sohn, Kihyuk, et al. **"Fixmatch: Simplifying semi-supervised learning with consistency and confidence."** Advances in neural information processing systems 33 (2020): 596-608.

## Image Classification

## Object Detection
- Ali, Mansoor, Gilberto Ochoa-Ruiz, and Sharib Ali. **"A semi-supervised Teacher-Student framework for surgical tool detection and localization."** arXiv preprint arXiv:2208.09926 (2022).

## Image Segmentation

## Panoptic Segmentation
- Li, Yanwei, et al. **"Fully convolutional networks for panoptic segmentation with point-based supervision."** IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).
- Shen, Yunhang, et al. **"Toward joint thing-and-stuff mining for weakly supervised panoptic segmentation."** Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
- Kirillov, Alexander, et al. **"Panoptic feature pyramid networks."** Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
- Li, Qizhu, Anurag Arnab, and Philip HS Torr. **"Weakly-and semi-supervised panoptic segmentation."** Proceedings of the European conference on computer vision (ECCV). 2018.
- Kirillov, Alexander, et al. **"Panoptic segmentation."** Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.

## Diffusion Models
-  Chai, Shang, Liansheng Zhuang, and Fengying Yan. **"LayoutDM: Transformer-based Diffusion Model for Layout Generation."** arXiv preprint arXiv:2305.02567 (2023).
- Bao, Fan, et al. **"All are Worth Words: a ViT Backbone for Score-based Diffusion Models."** arXiv preprint arXiv:2209.12152 (2022).
- You, Zebin, et al. **"Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels."** arXiv preprint arXiv:2302.10586 (2023).
- Bansal, Arpit, et al. **"Cold diffusion: Inverting arbitrary image transforms without noise."** arXiv preprint arXiv:2208.09392 (2022).
- Sohl-Dickstein, Jascha, et al. **"Deep unsupervised learning using nonequilibrium thermodynamics."** International Conference on Machine Learning. PMLR, 2015.
- Ho, Jonathan, Ajay Jain, and Pieter Abbeel. **"Denoising diffusion probabilistic models."** Advances in Neural Information Processing Systems 33 (2020): 6840-6851.
- Song, Jiaming, Chenlin Meng, and Stefano Ermon. **"Denoising diffusion implicit models."** arXiv preprint arXiv:2010.02502 (2020).
- Nichol, Alexander Quinn, and Prafulla Dhariwal. **"Improved denoising diffusion probabilistic models."** International Conference on Machine Learning. PMLR, 2021.
- Dhariwal, Prafulla, and Alexander Nichol. **"Diffusion models beat gans on image synthesis."** Advances in Neural Information Processing Systems 34 (2021): 8780-8794.
- Ho, Jonathan, et al. **"Cascaded Diffusion Models for High Fidelity Image Generation."** J. Mach. Learn. Res. 23 (2022): 47-1.

## GANs

## Image Synthsis
- Saseendran, Amrutha, Kathrin Skubch, and Margret Keuper. **"Multi-Class Multi-Instance Count Conditioned Adversarial Image Generation."** Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.
- Sylvain, Tristan, et al. **"Object-centric image generation from layouts."** Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 3. 2021.
- Koturwar, Saiprasad, Soma Shiraishi, and Kota Iwamoto. **"Robust multi-object detection based on data augmentation with realistic image synthesis for point-of-sale automation."** Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019.
- Hinz, Tobias, Stefan Heinrich, and Stefan Wermter. **"Generating multiple objects at spatially distinct locations."** arXiv preprint arXiv:1901.00686 (2019).

## Transformers
- Rao, Yongming, et al. **"Dynamicvit: Efficient vision transformers with dynamic token sparsification."** Advances in neural information processing systems 34 (2021): 13937-13949.

## Domain Adaptation
- Tzeng, Eric, et al. "Adversarial discriminative domain adaptation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
- Ganin, Yaroslav, et al. "Domain-adversarial training of neural networks." The journal of machine learning research 17.1 (2016): 2096-2030.

## Others
- Faghri, Fartash, et al. **"Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement."** arXiv preprint arXiv:2303.08983 (2023).

# Meta Learning
## Meta Learning
- (MAML): Finn, Chelsea, Pieter Abbeel, and Sergey Levine. **"Model-agnostic meta-learning for fast adaptation of deep networks."** International Conference on Machine Learning. PMLR, 2017.
- (Reptile): Nichol, Alex, Joshua Achiam, and John Schulman. **"On first-order meta-learning algorithms."** arXiv preprint arXiv:1803.02999 (2018).
- PEARL: Rakelly, Kate, et al. **"Efficient off-policy meta-reinforcement learning via probabilistic context variables." International conference on machine learning.** PMLR, 2019.
- MAML++: Antoniou, Antreas, Harrison Edwards, and Amos Storkey. **"How to train your MAML."** arXiv preprint arXiv:1810.09502 (2018).
- MQL: Fakoor, Rasool, et al. **"Meta-q-learning."** arXiv preprint arXiv:1910.00125 (2019).

## Meta MARL
- Parisotto, Emilio, et al. **"Concurrent meta reinforcement learning."** arXiv preprint arXiv:1903.02710 (2019).
- Chen, Long, et al. **"Multiagent Meta-Reinforcement Learning for Adaptive Multipath Routing Optimization."** IEEE Transactions on Neural Networks and Learning Systems (2021).
- Munir, Md Shirajum, et al. **"Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems."** IEEE Transactions on Network and Service Management (2021).
- Gupta, Abhinav, Angeliki Lazaridou, and Marc Lanctot. **"Meta Learning for Multi-agent Communication."** Learning to Learn-Workshop at ICLR 2021. 2021.

## Offline Meta
- Mitchell, Eric, et al. **"Offline Meta-Reinforcement Learning with Advantage Weighting."** arXiv preprint arXiv:2008.06043 (2020).
- Li, Lanqing, Rui Yang, and Dijun Luo. **"FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization."** arXiv preprint arXiv:2010.01112 (2020).

## Imitation Learning
- Duan, Yan, et al. **"One-shot imitation learning."** arXiv preprint arXiv:1703.07326 (2017).
- James, Stephen, Michael Bloesch, and Andrew J. Davison. **"Task-embedded control networks for few-shot imitation learning."** Conference on Robot Learning. PMLR, 2018.

## Traffic Applications
- Jaafra, Yesmina, et al. **"Meta-Reinforcement Learning for Adaptive Autonomous Driving."** (2019)
- Ye, Fei, et al. **"Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles."** arXiv preprint arXiv:2008.12451 (2020).  
- Hu, Ye, et al. **"Distributed multi-agent meta learning for trajectory design in wireless drone networks."** IEEE Journal on Selected Areas in Communications (2021).

# Power System
## Smart Buildings
- Vázquez-Canteli, José R., et al. **"Citylearn v1. 0: An openai gym environment for demand response with deep reinforcement learning."** Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. 2019.
- Zhang, Huiliang, Di Wu, and Benoit Boulet. **"MetaEMS: A Meta Reinforcement Learning-based Control Framework for Building Energy Management System."** arXiv preprint arXiv:2210.12590 (2022).
- Z. Nagy, G. Henze, S. Dey et al., **Ten questions concerning reinforcement learning for building energy management**, Building and Environment (2023), doi: https://doi.org/10.1016/j.buildenv.2023.110435.

## Voltage and Frequency Control
- Cao, Di, et al. "A multi-agent deep reinforcement learning based voltage regulation using coordinated PV inverters." IEEE Transactions on Power Systems 35.5 (2020): 4120-4123.
- 
- Wang, Minrui, et al. **"Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer."** arXiv preprint arXiv:2206.03721 (2022).
- Gao, Yuanqi, Wei Wang, and Nanpeng Yu. **"Consensus multi-agent reinforcement learning for volt-var control in power distribution networks."** IEEE Transactions on Smart Grid 12.4 (2021): 3594-3604.
- Wang, Jianhong, et al. **"Multi-agent reinforcement learning for active voltage control on power distribution networks."** Advances in Neural Information Processing Systems 34 (2021): 3271-3284.
- Zhang, Qianzhi, et al. **"Multi-agent safe policy learning for power management of networked microgrids."** IEEE Transactions on Smart Grid 12.2 (2020): 1048-1062.

## Energy Trading
- Qiu, Dawei, et al. **"Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer Multi-Energy Trading."** IEEE Transactions on Power Systems (2022).
- Chen, Tianyi, et al. **"Peer-to-peer energy trading and energy conversion in interconnected multi-energy microgrids using multi-agent deep reinforcement learning."** IEEE Transactions on Smart Grid 13.1 (2021): 715-727.
- Ye, Yujian, et al. **"A scalable privacy-preserving multi-agent deep reinforcement learning approach for large-scale peer-to-peer transactive energy trading."** IEEE transactions on smart grid 12.6 (2021): 5185-5200.

## Testbed
- Meinecke, Steffen, et al. **"Simbench—a benchmark dataset of electric power systems to compare innovative solutions based on power flow analysis."** Energies 13.12 (2020): 3290.

## Load Control
- Qin, Zhaoming, et al. **"Privacy preserving load control of residential microgrid via deep reinforcement learning."** IEEE Transactions on Smart Grid 12.5 (2021): 4079-4089.

# Precision Agriculture
## Weed Control
- Dang, Fengying, et al. **"YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems."** Computers and Electronics in Agriculture 205 (2023): 107655.
- Steininger, Daniel, et al. **"The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation."** Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2023.
- Rahman, Abdur, Yuzhen Lu, and Haifeng Wang. **"Performance Evaluation of Deep Learning Object Detectors for Weed Detection for Cotton."** Smart Agricultural Technology (2022): 100126.
- Du, Yayun, et al. **"Deep-cnn based robotic multi-class under-canopy weed control in precision farming."** 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022.
- Chen, Dong, et al. **"Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems."** Computers and Electronics in Agriculture 198 (2022): 107091.
- Dang, Fengying, et al. **"DeepCottonWeeds (DCW): A Novel Benchmark of YOLO Object Detectors for Weed Detection in Cotton Production Systems."** 2022 ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers, 2022.

## Plant Disease
- Paymode, Ananda S., and Vandana B. Malode. **"Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG."** Artificial Intelligence in Agriculture 6 (2022): 23-33.

## Fruit Detection
- Li, Kangshun, et al. **"A fast and lightweight detection algorithm for passion fruit pests based on improved YOLOv5."** Computers and Electronics in Agriculture 204 (2023): 107534.

## Fruit Flowers
- Siddique, Abubakar, Amy Tabb, and Henry Medeiros. **"Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species."** arXiv preprint arXiv:2209.04618 (2022).

## Plant Phenotyping
- Roggiolani, Gianmarco, et al. **"Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain."** arXiv preprint arXiv:2210.07879 (2022).

## GANs in Agriculture
- Please refer to [Awesome GANs in Agriculture](https://github.com/DongChen06/GANs-Agriculture)

## Label-efficient Learning in Agriculture
- Please refer to [Label-efficient_in_Agriculture](https://github.com/DongChen06/Label-efficient_in_Agriculture)

## Data Augmentation
- Trabucco, Brandon, et al. **"Effective Data Augmentation With Diffusion Models."** arXiv preprint arXiv:2302.07944 (2023).
- [Survey] Lu, Yuzhen, et al. **"Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review."** Computers and Electronics in Agriculture 200 (2022): 107208. 
- [Survey] Xu, Mingle, et al. **"A Comprehensive Survey of Image Augmentation Techniques for Deep Learning."** arXiv preprint arXiv:2205.01491 (2022).

## Meat Science
- Lee, Hyo-Jun, et al. **"MSENet: Marbling score estimation network for automated assessment of Korean beef."** Meat Science 188 (2022): 108784.

# Agricultural Robots
- Hu, Chengsong, et al. **"Algorithm and System Development for Robotic Micro-Volume Herbicide Spray Towards Precision Weed Management."** IEEE Robotics and Automation Letters 7.4 (2022): 11633-11640.

# Robotics
## Soft Robots
- Liu, Wenbo, et al. **"Touchless interactive teaching of soft robots through flexible bimodal sensory interfaces."** Nature communications 13.1 (2022): 1-14.
- Xiao, Xuesu, et al. **"Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation."** arXiv preprint arXiv:2209.10780 (2022).
- Gasoto, Renato, et al. **"A validated physical model for real-time simulation of soft robotic snakes."** 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019.
- Liu, Xuan, et al. **"Learning to locomote with artificial neural-network and cpg-based control in a soft snake robot."** 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020.
- Liu, Xuan, Cagdas Onal, and Jie Fu. **"Reinforcement Learning of a CPG-regulated Locomotion Controller for a Soft Snake Robot."** arXiv preprint arXiv:2207.04899 (2022).
- Ji, Guanglin, et al. **"Towards Safe Control of Continuum Manipulator Using Shielded Multiagent Reinforcement Learning."** IEEE Robotics and Automation Letters 6.4 (2021): 7461-7468.
- Li, Guanda, Jun Shintake, and Mitsuhiro Hayashibe. **"Deep Reinforcement Learning Framework for Underwater Locomotion of Soft Robot."** 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021.
- Centurelli, Andrea, et al. **"Closed-loop Dynamic Control of a Soft Manipulator using Deep Reinforcement Learning."** IEEE Robotics and Automation Letters 7.2 (2022): 4741-4748.

# Tricks
## Ensemble
- [Esemble] Du, Xinqi, et al. **"A Contrastive-Enhanced Ensemble Framework for Efficient Multi-Agent Reinforcement Learning."** Expert Systems with Applications (2024): 123158.
- [Esemble] Lan, Qingfeng, et al. **"Maxmin q-learning: Controlling the estimation bias of q-learning."** arXiv preprint arXiv:2002.06487 (2020).
- [Esemble] Chen, Xinyue, et al. **"Randomized ensembled double q-learning: Learning fast without a model."** arXiv preprint arXiv:2101.05982 (2021).
- [Esemble] Hiraoka, Takuya, et al. **"Dropout Q-Functions for Doubly Efficient Reinforcement Learning."** arXiv preprint arXiv:2110.02034 (2021).

## Curriculum Learning
- Portelas, Rémy, et al. **"Automatic curriculum learning for deep rl: A short survey."** arXiv preprint arXiv:2003.04664 (2020).
- Chen, Dong, et al. **"Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic."** arXiv preprint arXiv:2105.05701 (2021).
- Liu, Xuan, et al. **"Learning to locomote with artificial neural-network and cpg-based control in a soft snake robot."** 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020.
- Zeng, Yilei, et al. **"Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment."** arXiv preprint arXiv:2208.02932 (2022).

## Contrastive Learning
- Gao, Tianyu, Xingcheng Yao, and Danqi Chen. **"Simcse: Simple contrastive learning of sentence embeddings."** arXiv preprint arXiv:2104.08821 (2021).
- Du, Xinqi, et al. **"A Contrastive-Enhanced Ensemble Framework for Efficient Multi-Agent Reinforcement Learning."** Expert Systems with Applications (2024): 123158.

## Auxiliary Task
- Zhou,Shumin, et al. **""Auxiliary Task-based Deep Reinforcement Learning for Quantum Control""**, arXiv preprint arXiv:2302.14312 (2023).
- Devlin, Sam, et al. **"Potential-based difference rewards for multiagent reinforcement learning."** Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems. 2014.

# Impressive Works
## Multi-agent Systems
- Kortvelesy, Ryan, Steven Morad, and Amanda Prorok. **"Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning."** arXiv preprint arXiv:2302.12826 (2023).
- Chen, Wubing. **"Learning Multi-intersection Traffic Signal Control via Coevolutionary Multi-Agent Reinforcement Learning."** (2023).

## Smart Agriculture
- Tao, Ran, et al. **"Optimizing crop management with reinforcement learning and imitation learning."** arXiv preprint arXiv:2209.09991 (2022).
- Steininger, Daniel, et al. **"The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation."** Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2023.

## Reinforcement Learning
- Das, Lokesh Chandra, and Myounggyu Won. **"Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning."** International Conference on Machine Learning. PMLR, 2021.
- Li, Yuecheng, Hongwen He, Amir Khajepour, Yong Chen, Weiwei Huo, and Hao Wang. **"Deep reinforcement learning for intelligent energy management systems of hybrid-electric powertrains: Recent advances, open issues, and prospects."** IEEE Transactions on Transportation Electrification (2024).

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