Papers

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 23)

Daily Paper Reading

Papers Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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).

Papers

Papers Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Papers

Papers Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README