scholarly journals Deep Reinforcement Learning Algorithms in Intelligent Infrastructure

2019 ◽  
Vol 4 (3) ◽  
pp. 52 ◽  
Author(s):  
Serrano
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Capital Expenditure ◽  
Smart Cities ◽  
Time Data ◽  
Intelligent Buildings ◽  
Operational Expenditure ◽  
Biological Algorithm ◽  
Biological Organisms

Intelligent infrastructure, including smart cities and intelligent buildings, must learn and adapt to the variable needs and requirements of users, owners and operators in order to be future proof and to provide a return on investment based on Operational Expenditure (OPEX) and Capital Expenditure (CAPEX). To address this challenge, this article presents a biological algorithm based on neural networks and deep reinforcement learning that enables infrastructure to be intelligent by making predictions about its different variables. In addition, the proposed method makes decisions based on real time data. Intelligent infrastructure must be able to proactively monitor, protect and repair itself: this includes independent components and assets working the same way any autonomous biological organisms would. Neurons of artificial neural networks are associated with a prediction or decision layer based on a deep reinforcement learning algorithm that takes into consideration all of its previous learning. The proposed method was validated against an intelligent infrastructure dataset with outstanding results: the intelligent infrastructure was able to learn, predict and adapt to its variables, and components could make relevant decisions autonomously, emulating a living biological organism in which data flow exhaustively.

scholarly journals Pre-Training Acquisition Functions by Deep Reinforcement Learning for Fixed Budget Active Learning

2021 ◽  
Author(s):  
Yusuke Taguchi ◽  
Hideitsu Hino ◽  
Keisuke Kameyama
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Active Learning ◽  
Supervised Learning ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
Learning Problem ◽  
Q Learning ◽  
Fixed Budget ◽  
Active Learner

AbstractThere are many situations in supervised learning where the acquisition of data is very expensive and sometimes determined by a user’s budget. One way to address this limitation is active learning. In this study, we focus on a fixed budget regime and propose a novel active learning algorithm for the pool-based active learning problem. The proposed method performs active learning with a pre-trained acquisition function so that the maximum performance can be achieved when the number of data that can be acquired is fixed. To implement this active learning algorithm, the proposed method uses reinforcement learning based on deep neural networks as as a pre-trained acquisition function tailored for the fixed budget situation. By using the pre-trained deep Q-learning-based acquisition function, we can realize the active learner which selects a sample for annotation from the pool of unlabeled samples taking the fixed-budget situation into account. The proposed method is experimentally shown to be comparable with or superior to existing active learning methods, suggesting the effectiveness of the proposed approach for the fixed-budget active learning.

scholarly journals Neural Networks With Motivation

2021 ◽  
Vol 14 ◽  
Author(s):  
Sergey A. Shuvaev ◽  
Ngoc B. Tran ◽  
Marcus Stephenson-Jones ◽  
Bo Li ◽  
Alexei A. Koulakov
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Addictive Behaviors ◽  
Learning Networks ◽  
Q Learning ◽  
Motivational States ◽  
Ongoing Behavior ◽  
Motivational Salience ◽  
Motivated Behaviors

Animals rely on internal motivational states to make decisions. The role of motivational salience in decision making is in early stages of mathematical understanding. Here, we propose a reinforcement learning framework that relies on neural networks to learn optimal ongoing behavior for dynamically changing motivation values. First, we show that neural networks implementing Q-learning with motivational salience can navigate in environment with dynamic rewards without adjustments in synaptic strengths when the needs of an agent shift. In this setting, our networks may display elements of addictive behaviors. Second, we use a similar framework in hierarchical manager-agent system to implement a reinforcement learning algorithm with motivation that both infers motivational states and behaves. Finally, we show that, when trained in the Pavlovian conditioning setting, the responses of the neurons in our model resemble previously published neuronal recordings in the ventral pallidum, a basal ganglia structure involved in motivated behaviors. We conclude that motivation allows Q-learning networks to quickly adapt their behavior to conditions when expected reward is modulated by agent’s dynamic needs. Our approach addresses the algorithmic rationale of motivation and makes a step toward better interpretability of behavioral data via inference of motivational dynamics in the brain.

Expression of Continuous State and Action Spaces forQ-Learning Using Neural Networks and CMAC

2012 ◽  
Vol 24 (2) ◽  
pp. 330-339 ◽  
Author(s):  
Kazuaki Yamada ◽  
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Autonomous Robot ◽  
High Dimensional ◽  
Robot Arm ◽  
Mapping Functions ◽  
Continuous State ◽  
Dimensional Mapping

This paper proposes a new reinforcement learning algorithm that can learn, using neural networks and CMAC, a mapping function between highdimensional sensors and the motors of an autonomous robot. Conventional reinforcement learning algorithms require a lot of memory because they use lookup tables to describe high-dimensional mapping functions. Researchers have therefore tried to develop reinforcement learning algorithms that can learn the high-dimensional mapping functions. We apply the proposed method to an autonomous robot navigation problem and a multi-link robot arm reaching problem, and we evaluate the effectiveness of the method.

scholarly journals Autonomous Vehicle Fuel Economy Optimization with Deep Reinforcement Learning

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1911
Author(s):  
Hyunkun Kim ◽  
Hyeongoo Pyeon ◽  
Jong Sool Park ◽  
Jin Young Hwang ◽  
Sejoon Lim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Velocity Profile ◽  
Fuel Economy ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Simulation Program ◽  
On The Road

The ever-increasing number of vehicles on the road puts pressure on car manufacturers to make their car fuel-efficient. With autonomous vehicles, we can find new strategies to optimize fuels. We propose a reinforcement learning algorithm that trains deep neural networks to generate a fuel-efficient velocity profile for autonomous vehicles given road altitude information for the planned trip. Using a highly accurate industry-accepted fuel economy simulation program, we train our deep neural network model. We developed a technique for adapting the heterogeneous simulation program on top of an open-source deep learning framework, and reduced dimension of the problem output with suitable parameterization to train the neural network much faster. The learned model combined with reinforcement learning-based strategy generation effectively generated the velocity profile so that autonomous vehicles can follow to control itself in a fuel efficient way. We evaluate our algorithm’s performance using the fuel economy simulation program for various altitude profiles. We also demonstrate that our method can teach neural networks to generate useful strategies to increase fuel economy even on unseen roads. Our method improved fuel economy by 8% compared to a simple grid search approach.

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