Research on reinforcement learning-based safe decision-making methodology for multiple unmanned aerial vehicles [PDF]
Frontiers in Neurorobotics, 2023 A system with multiple cooperating unmanned aerial vehicles (multi-UAVs) can use its advantages to accomplish complicated tasks. Recent developments in deep reinforcement learning (DRL) offer good prospects for decision-making for multi-UAV systems ...
Longfei Yue +3 more
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Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State [PDF]
Entropy, 2021 In this paper, we consider a scenario where the base station (BS) collects time-sensitive data from multiple sensors through time-varying and error-prone channels.
Yuchao Chen +3 more
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Controllable Summarization with Constrained Markov Decision Process
Transactions of the Association for Computational Linguistics, 2021 We study controllable text summarization, which allows users to gain control on a particular attribute (e.g., length limit) of the generated summaries. In this work, we propose a novel training framework based on Constrained Markov Decision Process (CMDP)
Hou Pong Chan, Lu Wang, Irwin King
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Adaptive Cruise Control Based on Safe Deep Reinforcement Learning [PDF]
SensorsAdaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-
Rui Zhao +5 more
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Constrained Soft Actor–Critic for Joint Computation Offloading and Resource Allocation in UAV-Assisted Edge Computing [PDF]
SensorsUnmanned Aerial Vehicle (UAV)-assisted edge computing supports latency-sensitive applications by offloading computational tasks to ground-based servers.
Nawazish Muhammad Alvi +4 more
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SADQN-Based Residual Energy-Aware Beamforming for LoRa-Enabled RF Energy Harvesting for Disaster-Tolerant Underground Mining Networks [PDF]
SensorsThe end-to-end efficiency of radio-frequency (RF)-powered wireless communication networks (WPCNs) in post-disaster underground mine environments can be enhanced through adaptive beamforming.
Hilary Kelechi Anabi +2 more
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Towards Robust Decision-Making for Autonomous Highway Driving Based on Safe Reinforcement Learning [PDF]
SensorsReinforcement Learning (RL) methods are regarded as effective for designing autonomous driving policies. However, even when RL policies are trained to convergence, ensuring their robust safety remains a challenge, particularly in long-tail data ...
Rui Zhao +4 more
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Network switching strategy for energy conservation in heterogeneous networks. [PDF]
PLoS ONE, 2017 In heterogeneous networks (HetNets), the large-scale deployment of small base stations (BSs) together with traditional macro BSs is an economical and efficient solution that is employed to address the exponential growth in mobile data traffic.
Yujae Song, Wooyeol Choi, Seungjae Baek
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User-Perceived Capacity: Theory, Computation, and Achievable Policies [PDF]
EntropyUser-perceived throughput is a novel performance metric attracting a considerable amount of recent attention because it characterizes the quality of the experience in mobile multimedia services.
Yuanrui Liu, Xiaoyu Zhao, Wei Chen
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Federated reinforcement learning with constrained markov decision processes and graph neural networks for fair and grid-constrained coordination of large-scale electric vehicle charging networks [PDF]
Scientific ReportsThe rapid proliferation of electric vehicles (EVs) and their spatially clustered charging behaviors have imposed unprecedented challenges on the stability, efficiency, and fairness of power distribution networks.
Lixia Zhou +4 more
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