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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 04562naa a2200637 4500 | |
| 001 | oai:DiVA.org:kth-325693 | |
| 003 | SwePub | |
| 008 | 230412s2022 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3256932 URI |
| 024 | 7 | a https://doi.org/10.1109/JIOT.2022.31870672 DOI |
| 040 | a (SwePub)kth | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Lei, Wanluu KTH,Teknisk informationsvetenskap,Interconnection Design in Baseband and Interconnect Department, Ericsson AB, Stockholm, Sweden4 aut0 (Swepub:kth)u1dm05jn |
| 245 | 1 0 | a Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge IoT |
| 264 | 1 | b Institute of Electrical and Electronics Engineers (IEEE),c 2022 |
| 338 | a print2 rdacarrier | |
| 500 | a QC 20230412 | |
| 520 | a Edge computing provides a promising paradigm to support the implementation of Internet of Things (IoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasing network size makes it impractical for centralized data processing due to limited bandwidth, and consequently a decentralized learning scheme is preferable. Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. For RL in a decentralized setup, edge nodes (agents) connected through a communication network aim to work collaboratively to find a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability, and adaptation in complex environments with heterogeneous agents may significantly limit the performance of decentralized RL. Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation and has shown faster convergence than gradient descent-based methods. Therefore, we propose an adaptive stochastic incremental ADMM (asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL with edge-computing-empowered IoT networks. We provide convergence properties for the proposed algorithms by designing a Lyapunov function and prove that the asI-ADMM has O(1/k) + O(1/M) convergence rate, where k and M are the number of iterations and batch samples, respectively. Then, we test our algorithm with two supervised learning problems. For performance evaluation, we simulate two applications in decentralized RL settings with homogeneous and heterogeneous agents. The experimental results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability and can well adapt to complex IoT environments. | |
| 650 | 7 | a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Reglerteknik0 (SwePub)202022 hsv//swe |
| 650 | 7 | a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Control Engineering0 (SwePub)202022 hsv//eng |
| 653 | a Communication efficiency | |
| 653 | a decentralized edge computing | |
| 653 | a reinforcement learning (RL) | |
| 653 | a stochastic alternating direction method of multiplier (ADMM) | |
| 653 | a Complex networks | |
| 653 | a Data handling | |
| 653 | a Decision making | |
| 653 | a Edge computing | |
| 653 | a Gradient methods | |
| 653 | a Internet of things | |
| 653 | a Job analysis | |
| 653 | a Lyapunov functions | |
| 653 | a Random processes | |
| 653 | a Reinforcement learning | |
| 653 | a Scalability | |
| 653 | a Stochastic systems | |
| 653 | a Alternating directions method of multipliers | |
| 653 | a Convergence | |
| 653 | a Decentralised | |
| 653 | a Optimisations | |
| 653 | a Reinforcement learnings | |
| 653 | a Stochastic alternating direction method of multiplier | |
| 653 | a Stochastics | |
| 653 | a Task analysis | |
| 653 | a Optimization | |
| 700 | 1 | a Ye, Yuu KTH,Teknisk informationsvetenskap4 aut0 (Swepub:kth)u1y1pv31 |
| 700 | 1 | a Xiao, Ming,d 1975-u KTH,Teknisk informationsvetenskap4 aut0 (Swepub:kth)u1iq6n9a |
| 700 | 1 | a Skoglund, Mikael,d 1969-u KTH,Teknisk informationsvetenskap4 aut0 (Swepub:kth)u1dbnyps |
| 700 | 1 | a Han, Z.4 aut |
| 710 | 2 | a KTHb Teknisk informationsvetenskap4 org |
| 773 | 0 | t IEEE Internet of Things Journald : Institute of Electrical and Electronics Engineers (IEEE)g 9:22, s. 22958-22971q 9:22<22958-22971x 2327-4662 |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-325693 |
| 856 | 4 8 | u https://doi.org/10.1109/JIOT.2022.3187067 |
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