/Papers

Daily Paper Reading

Papers

Papers related to machine learning, deep learning and reinforcement learning

Fresh Papers

  • 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).
  • Ng, Andrew Y., Daishi Harada, and Stuart Russell. "Policy invariance under reward transformations: Theory and application to reward shaping." Icml. Vol. 99. 1999.
  • Ma, Hailan, et al. "Curriculum-based deep reinforcement learning for quantum control." IEEE Transactions on Neural Networks and Learning Systems (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.
  • 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).
  • 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.
  • Trabucco, Brandon, et al. "Effective Data Augmentation With Diffusion Models." arXiv preprint arXiv:2302.07944 (2023).
  • Wang, Liyuan, et al. "A Comprehensive Survey of Continual Learning: Theory, Method and Application." arXiv preprint arXiv:2302.00487 (2023).
  • Xu, Mengda, Manuela Veloso, and Shuran Song. "ASPiRe: Adaptive Skill Priors for Reinforcement Learning." arXiv preprint arXiv:2209.15205 (2022).
  • Pan, Ling, et al. "Plan better amid conservatism: Offline multi-agent reinforcement learning with actor rectification." International Conference on Machine Learning. PMLR, 2022.

Contents

Reinforcement Learning

Survey

Value-based

  • 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: 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

  • 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.

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).

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).

Applications

Intelligent Transportation Systems

  • 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

  • Evans, Benjamin, et al. "Accelerating Online Reinforcement Learning via Supervisory Safety Systems." arXiv preprint arXiv:2209.11082 (2022).

Quantum Control

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

Multi Agent Reinforcement Learning

Survey MARL

  • 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

  • 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).

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.

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

  • 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).

Computer Vision

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

  • 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.

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

Voltage and Frequency Control

  • Wang, Minrui, et al. "Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer." arXiv preprint arXiv:2206.03721 (2022).
  • 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

Label-efficient Learning 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

Emsemble

  • [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

  • 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).

Auxiliary Task

  • Zhou,Shumin, et al. ""Auxiliary Task-based Deep Reinforcement Learning for Quantum Control"", arXiv preprint arXiv:2302.14312 (2023).

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).

Smart Agriculture

  • 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.