… Reinforcement Learning-based approach which decomposes the original power constrained,
performance optimization … The RL at finer grain is able to improve the performance while …

… To address the first technical challenge in Remark 2, we adopt a model-free reinforcement
learning scheme called Qlearning [10], which allows us to learn the optimal control policy …

… deep reinforcement learning based approach, we define linear quadratic cost as the control
performance … policies, we compare the performance of deep reinforcement learning based …

… To meet the high throughput requirement, this paper proposes a novel deep reinforcement
learning (DRL)-based performance optimization framework for blockchain-enabled IIoT …

… To solve (13), we propose an attention network based reinforcement learning (RL) algorithm
that enables the BS to evaluate the importance of semantic triples and optimize the RB …

… a novel deep reinforcement learning (DRL) based performance optimization framework for
… In this framework, we first carry out the performance analysis for blockchain systems from …

… plant-wide performance optimization with … -sum optimization problems. The difficulties we
will face are how to model the plantwide performance optimization as a multiagent optimization …

… In this paper, a deep reinforcement learning optimization … a good balance between performance
and environmental issues. … The framework was optimized by maximizing electric power …

… the Deep Reinforcement Learning (DRL) crucial … performance than earlier options. The
DRL approach assesses whether to offload and which service to dump to improve performance …

… The problem is thus one of sequential decision-making, so as to optimize the long-term
performance. A first advantage of MDP solution techniques is their generality: they can handle …