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Proof and breaks inside the novels upon HIV/STI prevention interventions targeting migrants throughout receiving countries: a new scoping evaluate.
Namely, participants in business, engineering, and physical sciences fields were more influenced by the robots and aligned their answers closer to the robot's suggestion than did those in the life sciences and humanities professions. The discussions provide insight into the potential use of robot persuasion in social HRI task scenarios; in particular, considering the influence that a robot displaying emotional behaviors has when persuading people.This article focuses on multiagent distributed-constrained optimization problems in a dynamic environment, in which a group of agents aims to cooperatively optimize a sum of time-changing local cost functions subject to time-varying coupled constraints. Both the local cost functions and constraint functions are unrevealed to an individual agent until an action is submitted. We first investigate a gradient-feedback scenario, where each agent can access both values and gradients of cost functions and constraint functions owned by itself at the chosen action. Then, we design a distributed primal-dual online learning algorithm and show that the proposed algorithm can achieve the sublinear bounds for both the regret and constraint violations. Furthermore, we extend the gradient-feedback algorithm to a gradient-free setup, where an individual agent has only attained the values of local cost functions and constraint functions at two queried points near the selected action. We develop a bandit version of the previous method and give the explicitly sublinear bounds on the expected regret and expected constraint violations. The results indicate that the bandit algorithm can achieve almost the same performance as the gradient-feedback algorithm under wild conditions. Finally, numerical simulations on an electric vehicle charging problem demonstrate the effectiveness of the proposed algorithms.Training agents via deep reinforcement learning with sparse rewards for robotic control tasks in vast state space are a big challenge, due to the rareness of successful experience. To solve this problem, recent breakthrough methods, the hindsight experience replay (HER) and aggressive rewards to counter bias in HER (ARCHER), use unsuccessful experiences and consider them as successful experiences achieving different goals, for example, hindsight experiences. According to these methods, hindsight experience is used at a fixed sampling rate during training. However, this usage of hindsight experience introduces bias, due to a distinct optimal policy, and does not allow the hindsight experience to take variable importance at different stages of training. In this article, we investigate the impact of a variable sampling rate, representing the variable rate of hindsight experience, on training performance and propose a sampling rate decay strategy that decreases the number of hindsight experiences as training proceeds. Telratolimod order The proposed method is validated with three robotic control tasks included in the OpenAI Gym suite. The experimental results demonstrate that the proposed method achieves improved training performance and increased convergence speed over the HER and ARCHER with two of the three tasks and comparable training performance and convergence speed with the other one.This study aims to develop a novel wavelet neural-network (WNN) model for solving electrical resistivity imaging (ERI) inversion with massive amounts of measured data in control and measurement fields. In the proposed method, we design a mixed multilayer WNN (MMWNN) which uses Morlet and Mexican wavelons as different activation functions in a cascaded hidden layer structure. Meanwhile, a hybrid STGWO-GD learning approach is used to improve the learning ability of the MMWNN, which is a combination of the self-tuning grey wolf optimizer (STGWO) and the gradient descent (GD) algorithm adopting the advantages of each other. Moreover, updating formulas of the GD algorithm are derived, and a Gaussian updating operator with weighted hierarchical hunting, a chaotic oscillation equation, and a nonlinear modulation coefficient are introduced to improve the hierarchical hunting and the control parameter adjustment of the modified STGWO. Five examples are used with the aim of assessing the availability and feasibility of the proposed inversion method. The inversion results are promising and show that the introduced method is superior to other competitors in terms of inversion accuracy and computational efficiency. Furthermore, the effectiveness of the proposed method is demonstrated over a classical benchmark successfully.The problem of solving discrete-time Lyapunov equations (DTLEs) is investigated over multiagent network systems, where each agent has access to its local information and communicates with its neighbors. To obtain a solution to DTLE, a distributed algorithm with uncoordinated constant step sizes is proposed over time-varying topologies. The convergence properties and the range of constant step sizes of the proposed algorithm are analyzed. Moreover, a linear convergence rate is proved and the convergence performances over dynamic networks are verified by numerical simulations.Many real-world optimization problems involve multiple objectives, constraints, and parameters that may change over time. These problems are often called dynamic multiobjective optimization problems (DMOPs). The difficulty in solving DMOPs is the need to track the changing Pareto-optimal front efficiently and accurately. It is known that transfer learning (TL)-based methods have the advantage of reusing experiences obtained from past computational processes to improve the quality of current solutions. However, existing TL-based methods are generally computationally intensive and thus time consuming. This article proposes a new memory-driven manifold TL-based evolutionary algorithm for dynamic multiobjective optimization (MMTL-DMOEA). The method combines the mechanism of memory to preserve the best individuals from the past with the feature of manifold TL to predict the optimal individuals at the new instance during the evolution. The elites of these individuals obtained from both past experience and future prediction will then constitute as the initial population in the optimization process.
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