scholarly journals Adaptive Facial Imagery Clustering via Spectral Clustering and Reinforcement Learning

2021 ◽  
Vol 11 (17) ◽  
pp. 8051
Author(s):  
Chengxiao Shen ◽  
Liping Qian ◽  
Ningning Yu
Keyword(s):  
Reinforcement Learning ◽  
Spectral Clustering ◽  
Practical Importance ◽  
Optimal Number ◽  
Q Learning ◽  
Face Images ◽  
Clustering Model ◽  
Clustering Quality ◽  
Evaluation Measure ◽  
Feedback Module

In an era of big data, face images captured in social media and forensic investigations, etc., generally lack labels, while the number of identities (clusters) may range from a few dozen to thousands. Therefore, it is of practical importance to cluster a large number of unlabeled face images into an efficient range of identities or even the exact identities, which can avoid image labeling by hand. Here, we propose adaptive facial imagery clustering that involves face representations, spectral clustering, and reinforcement learning (Q-learning). First, we use a deep convolutional neural network (DCNN) to generate face representations, and we adopt a spectral clustering model to construct a similarity matrix and achieve clustering partition. Then, we use an internal evaluation measure (the Davies–Bouldin index) to evaluate the clustering quality. Finally, we adopt Q-learning as the feedback module to build a dynamic multiparameter debugging process. The experimental results on the ORL Face Database show the effectiveness of our method in terms of an optimal number of clusters of 39, which is almost the actual number of 40 clusters; our method can achieve 99.2% clustering accuracy. Subsequent studies should focus on reducing the computational complexity of dealing with more face images.

scholarly journals Personalized project recommendations: using reinforcement learning

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Faxin Qi ◽  
Xiangrong Tong ◽  
Lei Yu ◽  
Yingjie Wang
Keyword(s):  
Reinforcement Learning ◽  
User Behavior ◽  
Collaborative Work ◽  
Recursive Least Squares ◽  
The Internet ◽  
Dynamic Impact ◽  
Rls Algorithm ◽  
Trust Value ◽  
Q Learning ◽  
Actual Evaluation

AbstractWith the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.

scholarly journals Integrating Production Planning with Truck-Dispatching Decisions through Reinforcement Learning While Managing Uncertainty

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 587
Author(s):  
Joao Pedro de Carvalho ◽  
Roussos Dimitrakopoulos
Keyword(s):  
Reinforcement Learning ◽  
Discrete Event ◽  
Mining Operations ◽  
Fixed Sequence ◽  
Q Learning ◽  
Reward Function ◽  
Copper Gold ◽  
Mining Complex ◽  
Learning Reinforcement ◽  
Operational Plan

This paper presents a new truck dispatching policy approach that is adaptive given different mining complex configurations in order to deliver supply material extracted by the shovels to the processors. The method aims to improve adherence to the operational plan and fleet utilization in a mining complex context. Several sources of operational uncertainty arising from the loading, hauling and dumping activities can influence the dispatching strategy. Given a fixed sequence of extraction of the mining blocks provided by the short-term plan, a discrete event simulator model emulates the interaction arising from these mining operations. The continuous repetition of this simulator and a reward function, associating a score value to each dispatching decision, generate sample experiences to train a deep Q-learning reinforcement learning model. The model learns from past dispatching experience, such that when a new task is required, a well-informed decision can be quickly taken. The approach is tested at a copper–gold mining complex, characterized by uncertainties in equipment performance and geological attributes, and the results show improvements in terms of production targets, metal production, and fleet management.

scholarly journals Aircraft Maintenance Check Scheduling Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 113
Author(s):  
Pedro Andrade ◽  
Catarina Silva ◽  
Bernardete Ribeiro ◽  
Bruno F. Santos
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
Q Learning ◽  
Scheduling Policy ◽  
Real Scenario ◽  
Maintenance Plan ◽  
Small Disturbances

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.

scholarly journals Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 737
Author(s):  
Fengjie Sun ◽  
Xianchang Wang ◽  
Rui Zhang
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Optimal Policy ◽  
Feasible Solution ◽  
Learning Algorithm ◽  
Plant Protection ◽  
Agricultural Plant ◽  
Q Learning ◽  
Aerial Vehicle ◽  
Optimal Action

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.

scholarly journals Comparing quantum hybrid reinforcement learning to classical methods

2021 ◽  
Author(s):  
Maximilian Moll ◽  
Leonhard Kunczik
Keyword(s):  
Reinforcement Learning ◽  
Quantum Circuits ◽  
Memory Usage ◽  
Computational Power ◽  
Classical Domain ◽  
Q Learning ◽  
Optimal Behavior ◽  
Computational Speed ◽  
Complex Decision ◽  
Hybrid Reinforcement

AbstractIn recent history, reinforcement learning (RL) proved its capability by solving complex decision problems by mastering several games. Increased computational power and the advances in approximation with neural networks (NN) paved the path to RL’s successful applications. Even though RL can tackle more complex problems nowadays, it still relies on computational power and runtime. Quantum computing promises to solve these issues by its capability to encode information and the potential quadratic speedup in runtime. We compare tabular Q-learning and Q-learning using either a quantum or a classical approximation architecture on the frozen lake problem. Furthermore, the three algorithms are analyzed in terms of iterations until convergence to the optimal behavior, memory usage, and runtime. Within the paper, NNs are utilized for approximation in the classical domain, while in the quantum domain variational quantum circuits, as a quantum hybrid approximation method, have been used. Our simulations show that a quantum approximator is beneficial in terms of memory usage and provides a better sample complexity than NNs; however, it still lacks the computational speed to be competitive.

Improvement on Supporting Machine Learning Algorithm for Solving Problem in Immediate Decision Making

2012 ◽  
Vol 566 ◽  
pp. 572-579
Author(s):  
Abdolkarim Niazi ◽  
Norizah Redzuan ◽  
Raja Ishak Raja Hamzah ◽  
Sara Esfandiari
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Combined Model ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent ◽  
Case Base ◽  
Case Base Reasoning ◽  
Robotic Tool

In this paper, a new algorithm based on case base reasoning and reinforcement learning (RL) is proposed to increase the convergence rate of the reinforcement learning algorithms. RL algorithms are very useful for solving wide variety decision problems when their models are not available and they must make decision correctly in every state of system, such as multi agent systems, artificial control systems, robotic, tool condition monitoring and etc. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function is proposed to select the action, which led to an increase in algorithms based on Q-learning. The algorithm mentioned was used for solving the problem of cooperative Markov’s games as one of the models of Markov based multi-agent systems. The results of experiments Indicated that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

scholarly journals A proposal of privacy preserving reinforcement learning for secure multiparty computation

2017 ◽  
Vol 6 (2) ◽  
pp. 57 ◽  
Author(s):  
Hirofumi Miyajima ◽  
Noritaka Shigei ◽  
Syunki Makino ◽  
Hiromi Miyajima ◽  
Yohtaro Miyanishi ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Distributed Processing ◽  
Data Encryption ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Learning Methods ◽  
Q Learning ◽  
Encryption And Decryption ◽  
Maze Problem ◽  
Learning Data

Many studies have been done with the security of cloud computing. Though data encryption is a typical approach, high computing complexity for encryption and decryption of data is needed. Therefore, safe system for distributed processing with secure data attracts attention, and a lot of studies have been done. Secure multiparty computation (SMC) is one of these methods. Specifically, two learning methods for machine learning (ML) with SMC are known. One is to divide learning data into several subsets and perform learning. The other is to divide each item of learning data and perform learning. So far, most of works for ML with SMC are ones with supervised and unsupervised learning such as BP and K-means methods. It seems that there does not exist any studies for reinforcement learning (RL) with SMC. This paper proposes learning methods with SMC for Q-learning which is one of typical methods for RL. The effectiveness of proposed methods is shown by numerical simulation for the maze problem.

scholarly journals A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

2021 ◽  
Vol 15 (12) ◽  
pp. 65
Author(s):  
Akindele Segun Afolabi ◽  
Shehu Ahmed ◽  
Olubunmi Adewale Akinola
Keyword(s):  
Reinforcement Learning ◽  
Power Level ◽  
Heterogeneous Wireless Networks ◽  
Interference Management ◽  
Base Station ◽  
User Equipment ◽  
Base Stations ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Macro Cell

<span lang="EN-US">Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using macrocell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. This paper developed a reinforcement learning model that assisted in coordinating interference in a heterogeneous network comprising macro-cell and pico-cell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the proposed Q-learning scheme improved the performances of average user equipment throughput in the network. In particular, </span><span lang="EN-US">multi-agent systems with a normal learning rate increased the throughput of associated user equipment by a whooping 212.5% compared to a macrocell-only scheme.</span>

scholarly journals A Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Huashuai Zhang ◽  
Tingmei Wang ◽  
Haiwei Shen
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Data Traffic ◽  
Wireless Data ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Q Learning ◽  
Dense Networks ◽  
Target Network ◽  
Wireless Resource Allocation

The resource optimization of ultra-dense networks (UDNs) is critical to meet the huge demand of users for wireless data traffic. But the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. This paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (DRL), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep Q-learning process. To effectively allocate resources in UDNs, the DRL algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep Q-network (DQN), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the Q value. Simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. Thus, it is very meaningful to introduce the DRL to the research of UDN resource allocation.

scholarly journals Reinforcement Learning using Convolutional Neural Network for Game Prediction

2020 ◽  
Vol 9 (8) ◽  
pp. 425-430
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Computer Games ◽  
Elevated Level ◽  
Q Learning ◽  
The Third ◽  
Level Information ◽  
Simple Neural Network ◽  
Fully Connected ◽  
Deep Learning Model

The paper presents a Deep learning model for playing computer games with elevated level information utilizing Reinforcement learning learning. The games are activity restricted (like snakes, catcher, air-bandit and so on.). The implementation is progressive in three parts. The first part deals with a simple neural network, the second one with Deep Q network and further to increase the accuracy and speed of the algorithm, the third part consists of a model consisting of convolution neural network for image processing and giving outputs from the fully connected layers so as to estimate the probability of an action being taken based on information extracted from inputs where we apply Q-learning to determine the best possible move. The results are further analysed and compared to provide an overview of the improvements in each methods.

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