<b>REINFORCEMENT LEARNING: AN INTRODUCTION </b>by Richard S. Sutton and 
Andrew G. Barto, Adaptive Computation and Machine Learning series, MIT Press (Bradford Book), Cambridge, Mass., 1998, xviii + 322 pp, ISBN 0-262-19398-1, (hardback, £31.95).

Andrew, Alex M.

doi:10.1017/s0263574799211174

Cited by 29 publications

(20 citation statements)

References 1 publication

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“…To overcome the problem of continuous spaces, and to provide a generalization of different observed values, fitted Q iteration [112] is used instead of temporal difference learning [113]. The solution is evaluated for a small company with an EV fleet of 15 EVs where there are 4 EVs in the morning shift, which starts from 6:00 till 14:00.…”

Section: Centralized Day-ahead Planningmentioning

confidence: 99%

Reinforcement Learning Based EV Charging Management Systems–A Review

2021

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To mitigate global warming and energy shortage, integration of renewable energy generation sources, energy storage systems, and plug-in electric vehicles (PEVs) have been introduced in recent years. The application of electric vehicles (EV) in the smart grid has shown a significant option to reduce carbon emission. However, due to the limited battery capacity, managing the charging and discharging process of EV as a distributed power supply is a challenging task. Moreover, the unpredictable nature of renewable energy generation, uncertainties of plug-in electric vehicles associated parameters, energy prices, and the time-varying load create new challenges for the researchers and industries to maintain a stable operation of the power system. The EV battery charging management system plays a main role in coordinating the charging and discharging mechanism to efficiently realize a secure, efficient, and reliable power system. More recently, there has been an increasing interest in data-driven approaches in EV charging modeling. Consequently, researchers are looking to deploy model-free approaches for solving the EV charging management with uncertainties. Among many existing model-free approaches, Reinforcement Learning (RL) has been widely used for EV charging management. Unlike other machine learning approaches, the RL technique is based on maximizing the cumulative reward. This paper reviews the existing literature related to the RL-based framework, objectives, and architecture for the charging coordination strategies of electric vehicles in the power systems. In addition, the review paper presents a detailed comparative analysis of the techniques used for achieving different charging coordination objectives while satisfying multiple constraints. This paper also focuses on the application of RL in EV coordination for research and development of the cutting-edge optimized energy management system (EMS), which are applicable for EV charging.

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Section: Centralized Day-ahead Planningmentioning

confidence: 99%

Reinforcement Learning Based EV Charging Management Systems–A Review

2021

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“…where Q(s t , a t ) = E[R t | s t , a t ] is the state-action value function, in which the initial action a t is provided to calculate the expected return when starting in the state s t . A baseline function b(s t ) is typically subtracted to reduce the variance while not changing the estimated gradient [44,53]. A natural candidate for this baseline is the state only value function V (s t ) = E[R t | s t ], which is similar to Q(s t , a t ), except the a t is not given here.…”

Section: Inner-set Dependency Controlmentioning

confidence: 99%

Dependency-Aware Attention Control for Unconstrained Face Recognition with Image Sets

Liu

Kumar

Yang

et al. 2018

Computer Vision – ECCV 2018

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This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogeneous contents containing orderless images and videos. The importance of each image is usually considered either equal or based on their independent quality assessment. How to model the relationship of orderless images within a set remains a challenge. We address this problem by formulating it as a Markov Decision Process (MDP) in the latent space. Specifically, we first present a dependency-aware attention control (DAC) network, which resorts to actor-critic reinforcement learning for sequential attention decision of each image embedding to fully exploit the rich correlation cues among the unordered images. Moreover, we introduce its sample-efficient variant with off-policy experience replay to speed up the learning process. The pose-guided representation scheme can further boost the performance at the extremes of the pose variation.

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“…Where policy structure (the action which the agent uses to evaluate the next action strategy depends on the current state) is recognized as an actor, for the reason that it is employed to choose actions, and determined value of a function can be known as the critic, due to it criticizes actions that made by the actor. The critic has to observe and justify if the policy is being followed by way of the actor or not [37].…”

Section: Rlpbamentioning

confidence: 99%

Optimized Intelligent Design for Smart Systems Hybrid Beamforming and Power Adaptation Algorithms for Sensor Networks Decision-Making Approach

Khiarullah¹,

Tureli²,

Kivanc³

2019

IJCIS

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During last two decades, power adaptation and beamforming solutions have been proposed for multiple input multiple output (MIMO) Ad Hoc networks. Game theory based methods such as cooperative and non-cooperative joint beamforming and power control for the MIMO ad hoc systems consider the interference and overhead reduction, but have failed to achieve the tradeoff between communication overhead and power minimization. Cooperative method using game theory achieves the power minimization, but introduced the overhead. The non-cooperative solution using game theory reduced the overhead, but it takes more power and iterations for convergence. In this paper, a novel game theory based algorithms proposed to achieve the tradeoff between power control and communication overhead for multiple antennas enabled wireless ad-hoc networks operating in multiple-users interference environment. The optimized joint iterative power adaption and beamforming method designed to minimize the mutual interference at every wireless node with constant received signal to interference noise ratio (SINR) at every receiver node. First cooperative potential game theory based algorithm designed for the power and interference minimization in which users cluster and binary weight books along used to reduce the overhead. Then the non-cooperative based approach using the reinforcement learning (RL) method is proposed to reduce the number of iterations and power consumption in networks, the proposed RL procedure is fully distributed as every transmit node require only an observation of its instantaneous beamformer label which can be obtained from its receive node. The simulation results of both methods prove the efficient power adaption and beamforming for small and large networks with minimum overhead and interference compared to state-of-art methods.

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REINFORCEMENT LEARNING: AN INTRODUCTION by Richard S. Sutton and Andrew G. Barto, Adaptive Computation and Machine Learning series, MIT Press (Bradford Book), Cambridge, Mass., 1998, xviii + 322 pp, ISBN 0-262-19398-1, (hardback, £31.95).

Cited by 29 publications

References 1 publication

Reinforcement Learning Based EV Charging Management Systems–A Review

Reinforcement Learning Based EV Charging Management Systems–A Review

Dependency-Aware Attention Control for Unconstrained Face Recognition with Image Sets

Optimized Intelligent Design for Smart Systems Hybrid Beamforming and Power Adaptation Algorithms for Sensor Networks Decision-Making Approach

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