Results 31 to 40 of about 5,156,964 (309)
Energies, 2021 In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environmental conditions and are applicable ...
Kostas Bavarinos +2 more
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Q-learning based strategy analysis of cyber-physical systems considering unequal cost
Intelligent and Converged Networks, 2023 This paper proposes a cyber security strategy for cyber-physical systems (CPS) based on Q-learning under unequal cost to obtain a more efficient and low-cost cyber security defense strategy with misclassification interference.
Xin Chen +5 more
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, 2020 This work has received funding from the EU Horizon 2020 research and innovation program under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under projects CEECIND/01811/2017 and UIDB/00760 ...
Vale, Zita, Pinto, Tiago
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Q-LVS: A Q-Learning-based Algorithm for Video Streaming in Peer-to-Peer Networks Considering a Token-Based Incentive Mechanism [PDF]
Journal of Artificial Intelligence and Data Mining, 2022 Peer-to-peer video streaming has reached great attention during recent years. Video streaming in peer-to-peer networks is a good way to stream video on the Internet due to the high scalability, high video quality, and low bandwidth requirements.
Z. Imanimehr
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IEEE Access, 2023 The current application of control theory is commonly carried out in systems with a model or known system dynamics. However, in practice this is a formidable task to achieve as not all state information can be known. The use of the Output Feedback (OPFB)
Adi Novitarini Putri +3 more
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CoRR, 2018 Distributional reinforcement learning (distributional RL) has seen empirical success in complex Markov Decision Processes (MDPs) in the setting of nonlinear function approximation. However, there are many different ways in which one can leverage the distributional approach to reinforcement learning.
Thang Doan, Bogdan Mazoure, Clare Lyle
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Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
CoRR, 2020 ICLR ...
Qingfeng Lan +3 more
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Sparse cooperative Q-learning [PDF]
Twenty-first international conference on Machine learning - ICML '04, 2004 Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents. In this paper we are interested in using Q-learning to learn the coordinated actions of a group of cooperative agents, using a sparse representation of the joint state-action space of the agents.
Kok, J.R., Vlassis, N.
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Continuous-Action Q-Learning [PDF]
Machine Learning, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
José del R. Millán +2 more
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OPTIMIZING QOS IN SELF ORGANIZING HETEROGENEOUS WIRELESS CELLULAR NETWORK USING FIREFLY ALGORITHM
ICTACT Journal on Communication Technology, 2022 Capacity and energy efficiency are crucial for next-generation wireless networks. Due to the dense deployment of base stations (BSs) in a heterogeneous network (HetNets), the consumption is from 60% to 80% of the total energy causing accentuated costs ...
Gajanan Uttam Patil +1 more
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