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pro vyhledávání: '"Distributed reinforcement learning"'
On-device control agents, especially on mobile devices, are responsible for operating mobile devices to fulfill users' requests, enabling seamless and intuitive interactions. Integrating Multimodal Large Language Models (MLLMs) into these agents enha
Externí odkaz:
http://arxiv.org/abs/2410.14803
Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learning
In this letter, we propose a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework for Medium Access Control (MAC) protocol design. Unlike centralized approaches, which rely on a single entity for decision-making, MADRL empowers individual
Externí odkaz:
http://arxiv.org/abs/2408.05884
Publikováno v:
IEEE Transactions on Service Computing, 2024
Microservices have transformed monolithic applications into lightweight, self-contained, and isolated application components, establishing themselves as a dominant paradigm for application development and deployment in public clouds such as Google an
Externí odkaz:
http://arxiv.org/abs/2407.10169
Akademický článek
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The Intelligent Transportation System (ITS) environment is known to be dynamic and distributed, where participants (vehicle users, operators, etc.) have multiple, changing and possibly conflicting objectives. Although Reinforcement Learning (RL) algo
Externí odkaz:
http://arxiv.org/abs/2403.08879
Autor:
Jineng Ren
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-18 (2024)
Abstract This paper proposes a gradient-based multi-agent actor-critic algorithm for off-policy reinforcement learning using importance sampling. Our algorithm is incremental with full gradients, and its complexity per iteration scales linearly with
Externí odkaz:
https://doaj.org/article/3fd1277c9b234751b5bfbbae5d5a0742
Deep reinforcement learning has successfully been applied for molecular discovery as shown by the Molecule Deep Q-network (MolDQN) algorithm. This algorithm has challenges when applied to optimizing new molecules: training such a model is limited in
Externí odkaz:
http://arxiv.org/abs/2312.01267
Publikováno v:
IEEE Access, Vol 12, Pp 25385-25396 (2024)
In this paper, we propose a novel RSU-assisted hybrid road-aware routing algorithm, RHRA-DRL, designed for urban vehicular networks to optimize real-time data delivery considering dynamic road conditions. The algorithm minimizes broadcast overhead an
Externí odkaz:
https://doaj.org/article/08eb4ebf326541b8bb254685072d0087
Distributed Deep Reinforcement Learning (DRL) aims to leverage more computational resources to train autonomous agents with less training time. Despite recent progress in the field, reproducibility issues have not been sufficiently explored. This pap
Externí odkaz:
http://arxiv.org/abs/2310.00036
Advanced Air Mobility (AAM) introduces a new, efficient mode of transportation with the use of vehicle autonomy and electrified aircraft to provide increasingly autonomous transportation between previously underserved markets. Safe and efficient navi
Externí odkaz:
http://arxiv.org/abs/2308.04958