Results 81 to 90 of about 13,256,052 (164)

Meta-Learning-Driven Adaptive Control for Multi-Exit DNN Splitting at the Edge

IoT
Early-exit deep neural networks (DNNs) can reduce edge-inference latency, but abrupt variations in wireless and computing resources can destabilize split-inference policies.
Luyao Wang, Jiahao Xie, Hao Hao, Huiling Shi   +3 more
doaj   +1 more source

Adaptive Constraint Regulation for Human Preference-Aware Safe Reinforcement Learning of On-Ramp Merging

Machines
Reinforcement learning (RL) has been widely utilized for decision-making in highway on-ramp merging scenarios. However, most existing methods incorporate safety through reward functions, which may allow autonomous vehicles to trade safety for higher ...
Jingjia Teng, Wenjie Huang, Shijie Yuan, Manjiang Hu, Hongmao Qin, Yang Li, Yougang Bian, Bai Li   +7 more
doaj   +1 more source

On Bellman's principle of optimality and Reinforcement learning for safety-constrained Markov decision process

, 2023
We study optimality for the safety-constrained Markov decision process which is the underlying framework for safe reinforcement learning. Specifically, we consider a constrained Markov decision process (with finite states and finite actions) where the ...
Wisniewski, Rafał, Misra, Rahul, Kallesøe, Carsten Skovmose   +2 more
core  

Energy Management for Ship Integrated Power Systems via Mode-Aware Safe Reinforcement Learning

Journal of Marine Science and Engineering
Energy management in ship integrated power systems (IPSs) requires real-time dispatch of diesel generators, battery storage, and shore power under strict operational constraints.
Qingchi Yao, Chunteng Bao, Cuihong Zhang, Xiang Lei   +3 more
doaj   +1 more source

Achieving Õ(1/ε) Sample Complexity for Constrained Markov Decision Process

We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making.
Jiang, Jiashuo, Ye, Yinyu
core  

Federated Safe Proximal Policy Optimization for Robust Low-Carbon Dispatch of Heterogeneous Multi-Park Electricity–Heat–Hydrogen Integrated Energy Systems

Energies
To achieve low-carbon and cost-effective operation of multi-park electricity–heat–hydrogen integrated energy systems (EHHSs), this paper proposes a low-carbon dispatch framework based on federated safe reinforcement learning.
Zijie Peng, Xiaohui Yang, Qianhua Xiao
doaj   +1 more source

Achieving $\tilde{O}(1/\epsilon)$ Sample Complexity for Constrained Markov Decision Process

We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making.
Jiang, Jiashuo, Ye, Yinyu
core  

Distribution-Agnostic Idle-Age-Aware Joint Channel and Packet-Size Learning in Cognitive Radio Networks

IEEE Open Journal of the Communications Society
Cognitive radio (CR) enables opportunistic spectrum access while protecting primary users (PUs). This opportunistic transmission is uncertain due to time-varying PU activity (unpredictable PU returns) and heterogeneous channel quality.
Haythem A. Bany Salameh, Malik Mohamed Umar   +1 more
doaj   +1 more source

Microgrid Operation Optimization Strategy Based on CMDP-D3QN-MSRM Algorithm

This paper addresses the microgrid operation optimization challenges arising from the variability in and uncertainty and complex power flow constraints of distributed power sources. A novel method is proposed, based on an improved Dual-Competitive Deep Q-
Qian Wei, Jiayu Kang, Yushun Zeng
core   +1 more source

AI-Driven Adaptive Encryption Framework for a Modular Hardware-Based Data Security Device: Conceptual Architecture, Formal Foundations, and Security Analysis

Applied Sciences
This paper presents a conceptual architecture for an AI-Driven Adaptive Encryption Device (AI-AED), a tri-modular hardware platform embodied in a registered industrial design. The device integrates a Secure Input Module, an AI-Enhanced Central Processing
Pruthviraj Pawar, Gregory Epiphaniou
doaj   +1 more source