Fetching the paper…
Reading the bibliography…
To improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a global model through collaboration among $N$ agents using policy gradient (PG) updates.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
Earlier work this paper cites.
Distributed learning in wireless sensor networks
Joel B. Predd, Sanjeev B. Kulkarni, and H. Vincent Poor · 2006
Earlier work this paper cites.
MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Data science and its relationship to big data and data-driven decision making
Foster Provost and Tom Fawcett · 2013
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Stochastic variance-reduced policy gradient
Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, and Marcello Restelli · 2018
Earlier work this paper cites.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2018
Earlier work this paper cites.
DeepPool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning
Abubakr O. Al-Abbasi, Arnob Ghosh, and Vaneet Aggarwal · 2019
Earlier work this paper cites.
PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Earlier work this paper cites.
VAFL: A method of vertical asynchronous federated learning
Tianyi Chen, Xiao Jin, Yuejiao Sun, and Wotao Yin · 2020
Earlier work this paper cites.
Natural policy gradient primal-dual method for constrained Markov decision processes
Dongsheng Ding, Kaiqing Zhang, Tamer Basar, and Mihailo Jovanovic · 2020
Earlier work this paper cites.
Q-GADMM: Quantized group ADMM for communication efficient decentralized machine learning
Anis Elgabli, Jihong Park, Amrit Singh Bedi, Chaouki Ben Issaid, Mehdi Bennis, and Vaneet Aggarwal · 2020
Earlier work this paper cites.
Momentum-based policy gradient methods
Feihu Huang, Shangqian Gao, Jian Pei, and Heng Huang · 2020
Earlier work this paper cites.
An improved analysis of (variance-reduced) policy gradient and natural policy gradient methods
Yanli Liu, Kaiqing Zhang, Tamer Basar, and Wotao Yin · 2020
Earlier work this paper cites.
Federated learning for wireless communications: Motivation, opportunities, and challenges
Solmaz Niknam, Harpreet S. Dhillon, and Jeffrey H. Reed · 2020
Earlier work this paper cites.
Neural policy gradient methods: Global optimality and rates of convergence
Lingxiao Wang, Qi Cai, Zhuoran Yang, and Zhaoran Wang · 2020
Earlier work this paper cites.
Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2020
Earlier work this paper cites.
Sample efficient policy gradient methods with recursive variance reduction
Pan Xu, Felicia Gao, and Quanquan Gu · 2020
Earlier work this paper cites.
Global convergence of policy gradient methods to (almost) locally optimal policies
Kaiqing Zhang, Alec Koppel, Hao Zhu, and Tamer Basar · 2020
Earlier work this paper cites.
Single-forking of coded subtasks for straggler mitigation
Ajay Badita, Parimal Parag, and Vaneet Aggarwal · 2021
Earlier work this paper cites.
Communication-efficient policy gradient methods for distributed reinforcement learning
Tianyi Chen, Kaiqing Zhang, Georgios B Giannakis, and Tamer Başar · 2021
Earlier work this paper cites.
Challenges of real-world reinforcement learning: Definitions, benchmarks and analysis
Gabriel Dulac-Arnold, Nir Levine, Daniel J Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, and Todd Hester · 2021
Earlier work this paper cites.
Single-timescale actor-critic provably finds globally optimal policy
Zuyue Fu, Zhuoran Yang, and Zhaoran Wang · 2021
Earlier work this paper cites.
Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
Cited alongside, same era.
ElegantRL-Podracer: Scalable and elastic library for cloud-native deep reinforcement learning
Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, and Michael I Jordan · 2021
Cited alongside, same era.
A survey on security and privacy of federated learning
Viraaji Mothukuri, Reza M. Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava · 2021
Cited alongside, same era.
Stable-baselines3: Reliable reinforcement learning implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann · 2021
Cited alongside, same era.
On the global optimum convergence of momentum-based policy gradient
Yuhao Ding, Junzi Zhang, and Javad Lavaei · 2022
Cited alongside, same era.
FedKL: Tackling data heterogeneity in federated reinforcement learning by penalizing KL divergence
Zhijie Xie and Shenghui Song · 2023
Later among the works it cites.
Infinistore: Elastic serverless cloud storage
Jingyuan Zhang, Ao Wang, Xiaolong Ma, Benjamin Carver, Nicholas John Newman, Ali Anwar, Lukas Rupprecht, Vasily Tarasov, Dimitrios Skourtis, Feng Yan, and Yue Cheng · 2023
Later among the works it cites.
Regret analysis of policy gradient algorithm for infinite horizon average reward Markov decision processes
Qinbo Bai, Washim Uddin Mondal, and Vaneet Aggarwal · 2024
Closest in time.
Key focus areas and enabling technologies for 6G
Christopher G Brinton, Mung Chiang, Kwang Taik Kim, David J Love, Michael Beesley, Morris Repeta, John Roese, Per Beming, Erik Ekudden, Clara Li, et al · 2024
Closest in time.
Asynchronous multi-model dynamic federated learning over wireless networks: Theory, modeling, and optimization
Zhan-Lun Chang, Seyyedali Hosseinalipour, Mung Chiang, and Christopher G. Brinton · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Federated reinforcement learning with environment heterogeneity
Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2022
Cited alongside, same era.
Federated reinforcement learning: Linear speedup under Markovian sampling
Sajad Khodadadian, Pranay Sharma, Gauri Joshi, and Siva Theja Maguluri · 2022
Cited alongside, same era.
Deep reinforcement learning for autonomous driving: A survey
B. Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A. Al Sallab, Senthil Yogamani, and Patrick Pérez · 2022
Cited alongside, same era.
Sharper convergence guarantees for asynchronous SGD for distributed and federated learning
Anastasiia Koloskova, Sebastian U. Stich, and Martin Jaggi · 2022
Cited alongside, same era.
Exploration in deep reinforcement learning: A survey
Pawel Ladosz, Lilian Weng, Minwoo Kim, and Hyondong Oh · 2022
Cited alongside, same era.
Distributed inverse constrained reinforcement learning for multi-agent systems
Shicheng Liu and Minghui Zhu · 2022
Cited alongside, same era.
Asynchronous SGD beats minibatch SGD under arbitrary delays
Konstantin Mishchenko, Francis Bach, Mathieu Even, and Blake E Woodworth · 2022
Cited alongside, same era.
Closest in time.
A hybrid stacking method for short-term price forecasting in electricity trading market
Jianlong Chen, Jue Xiao, and Wei Xu · 2024
Closest in time.
Totoro: A scalable federated learning engine for the edge
Cheng-Wei Ching, Xin Chen, Taehwan Kim, Bo Ji, Qingyang Wang, Dilma Da Silva, and Liting Hu · 2024
Closest in time.
Cross layer optimization and distributed reinforcement learning for wireless 360° video streaming
Anis Elgabli, Mohammed S Elbamby, Cristina Perfecto, Mounssif Krouka, Mehdi Bennis, and Vaneet Aggarwal · 2024
Closest in time.
Closing the gap: Achieving global convergence (last iterate) of actor-critic under Markovian sampling with neural network parametrization
Mudit Gaur, Amrit S. Bedi, Di Wang, and Vaneet Aggarwal · 2024
Closest in time.
Contextual analysis using deep learning for sensitive information detection
Yaxin Liang, Erdi Gao, Yuhan Ma, Qishi Zhan, Dan Sun, and Xingxin Gu · 2024
Closest in time.
Adaptive transaction sequence neural network for enhanced money laundering detection
Shiqing Long, Didi Yi, Mohan Jiang, Minghao Liu, Guanming Huang, and Junliang Du · 2024
Closest in time.
Improved sample complexity analysis of natural policy gradient algorithm with general parameterization for infinite horizon discounted reward Markov decision processes
Washim U Mondal and Vaneet Aggarwal · 2024
Closest in time.
The sample-communication complexity trade-off in federated Q-learning
Sudeep Salgia and Yuejie Chi · 2024
Closest in time.
Gemma: Open models based on Gemini research and technology
Gemma Team and Google DeepMind · 2024
Closest in time.
Personalized federated reinforcement learning with shared representations
Guojun Xiong, Shufan Wang, Daniel Jiang, and Jian Li · 2024
Closest in time.
CloudEval-YAML: A practical benchmark for cloud configuration generation
Yifei Xu, Yuning Chen, Xumiao Zhang, Xianshang Lin, Pan Hu, Yunfei Ma, Songwu Lu, Wan Du, Zhuoqing Mao, Ennan Zhai, et al · 2024
Closest in time.
Federated natural policy gradient and actor critic methods for multi-task reinforcement learning
Tong Yang, Shicong Cen, Yuting Wei, Yuxin Chen, and Yuejie Chi · 2024
Closest in time.
Communication-efficient multimodal federated learning: Joint modality and client selection
Liangqi Yuan, Dong-Jun Han, Su Wang, Devesh Upadhyay, and Christopher G Brinton · 2024
Closest in time.
Finite-time analysis of on-policy heterogeneous federated reinforcement learning
Chenyu Zhang, Han Wang, Aritra Mitra, and James Anderson · 2024
Closest in time.
SePPO: Semi-policy preference optimization for diffusion alignment
Daoan Zhang*, Guangchen Lan*, Dong-Jun Han, Wenlin Yao, Xiaoman Pan, Hongming Zhang, Mingxiao Li, Pengcheng Chen, Yu Dong, Christopher Brinton, and Jiebo Luo · 2024
Closest in time.
Zhong Zheng, Haochen Zhang, and Lingzhou Xue · 2024
Closest in time.
Towards fast rates for federated and multi-task reinforcement learning
Feng Zhu, Robert W Heath Jr, and Aritra Mitra · 2024
Closest in time.
Unlocking the potential of model calibration in federated learning
Yun-Wei Chu, Dong-Jun Han, Seyyedali Hosseinalipour, and Christopher G Brinton · 2025
Closest in time.
Order-optimal regret with novel policy gradient approaches in infinite horizon average reward mdps
Swetha Ganesh, Washim Uddin Mondal, and Vaneet Aggarwal · 2025
Closest in time.