Q Learning News Today : Breaking News, Live Updates & Top Stories | Vimarsana
Stay updated with breaking news from Q learning. Get real-time updates on events, politics, business, and more. Visit us for reliable news and exclusive interviews.
Top News In Q Learning Today - Breaking & Trending Today
This paper considers collection and processing of data by solar-powered servers operating in an Internet of Things (IoT) network. Specifically, these servers aim to cooperatively maximize the amount of data collected from devices and computed over multiple time slots. To achieve this aim, they must consider computation deadline, time-varying energy arrivals at sensor devices and other servers. To this end, this paper outlines a mixed integer linear program (MILP), which can be used to optimize the sensing time of sensor devices, offloading decision of each server, and the number of virtual machines (VMs) assigned to each device. Further, this paper proposes a multi-agent co-operative Q-learning approach coupled with the Hungarian algorithm to assign VMs to devices. It allows servers to learn when to share their energy and VMs with neighbor servers using only non-causal energy and channel gain information. The simulation results show that the amount of processed data by co-operative Q-l ....
In online reinforcement learning, operators predict the return by weighting the successors’ estimated value. However, due to the lack of uncertainty quantification, weights assigned by operators are affected by the potentially biased estimations. As a result, the partial order of estimated values is ineffective. To increase the probability of outputting an optimal partial order, this paper introduces the hedonistic expected value (HEV), an upper bound of the return's expectation to quantify the uncertainty. Notably, for compatibility reasons, some complex operators are rewritten as the weighted-sum forms. Based on the weighted-sum form of the operator, the variant Q-learning, namely uncertainty quantification based Q-learning is proposed in this paper. In the proposed algorithm, the weights assigned by HEV of the successors are compatible with the existing operators. The prediction of the return is not only the sum over the weights succeeding the operator but also over the weigh ....
Data collection is a fundamental operation in energy harvesting industrial Internet of Things networks. To this end, we consider a hybrid access point (HAP) or controller that is responsible for charging and collecting L bits from sensor devices. The problem at hand is to optimize the transmit power allocation of the HAP over multiple time frames. The main challenge is that the HAP has causal channel state information to devices. In this article, we outline a novel two-step reinforcement learning with Gibbs sampling (TSRL-Gibbs) strategy, where the first step uses Q-learning and an action space comprising transmit power allocation sampled from a multidimensional simplex. The second step applies Gibbs sampling to further refine the action space. Our results show that TSRL-Gibbs requires up to 28.5% fewer frames than competing approaches. ....
Purpose: User feedback inferred from the user's search-time behavior could improve the learning to rank (L2R) algorithms. Click models (CMs) present probabilistic frameworks for describing and predicting the user's clicks during search sessions. Most of these CMs are based on common assumptions such as Attractiveness, Examination and User Satisfaction. CMs usually consider the Attractiveness and Examination as pre- and post-estimators of the actual relevance. They also assume that User Satisfaction is a function of the actual relevance. This paper extends the authors' previous work by building a reinforcement learning (RL) model to predict the relevance. The Attractiveness, Examination and User Satisfaction are estimated using a limited number of the features of the utilized benchmark data set and then they are incorporated in the construction of an RL agent. The proposed RL model learns to predict the relevance label of documents with respect to a given query more effec ....
Recently, Wireless Body Area Networks (WBAN) have been developed to advance Internet-of-Things (IoT) that play an essential role in biomedical applications. While deploying these applications practically, there may arise associated issues. Among all the available problems, the primary concern is energy utilization among these resource-limited sensors during data communication. These sensors continuously sense the signal and send messages to other nodes. There is a need to optimize the energy utilization in WBAN. This paper proposes a cluster-based routing protocol for WBAN with the benefits of machine learning to predict energy wastage. A Modified Grey Wolf Optimization with Q-Learning (MGWOQL) is proposed for cluster head selection and updating. The proposed protocol used different objective functions to minimize the energy utilization of clusters by selecting the optimal cluster head (CH). The simulation was performed on the MATLAB platform under different conditions. The result anal ....