Sharif Digital Repository

Nowadays, video on demand causes a drastic increase in network traffic that it is expected that network traffic surpasses 45 exabytes per month until 2022; consequently, utilizing distributed memories known as caches across the network to alleviate the communication load during peak hours is inevitable. Coded caching is a promising approach to mitigate and smooth traffic during peak hours in the communication network in a way that it creates coded multicasting opportunities in addition to delivering content to users locally. However, it suffers from imposed delay resulting from coding that makes this approach infeasible for delay-sensitive contents, namely video streaming applications. So... 

The use of IoT-based technologies is expanding in many areas today. The use of audio and video processing in IoT systems has been used as an alternative to human operators by increasing power and reducing processing costs. Due to the large volume of audio and video data and bandwidth limitations, complete data transfer to cloud processing servers is not cost-effective in terms of efficiency and energy consumption. As a result, the solution that has provided good results is to discharge these device tasks to the available clouds. In other words, the capacity of resources in the environment can be used to optimize the total latency of the system and energy consumption. In this dissertation, we... 

Hybrid Exoskeletons refer to simultaneous use of wearable robots and functional electrical stimulation technology. Hybrid exoskeletons have many advantages compared to the separate application of each of these technologies, such as reducing the robot’s energy consumption and the need for lighter and cheaper actuators for the robot, using humans muscle power, and reducing muscle fatigue. As a result, these robots have recently attracted a lot of interest in rehabilitation applications for patients suffering from mobility impairment.Control in hybrid exoskeletons is more complicated than control in traditional exoskeletons. Because in addition to robot and functional electrical stimulation... 

Nowadays, with the remarkable development of the robotics industry, there is an increasing demand for mobile robots. Mobile robots can be deployed individually or in groups for various tasks such as autonomous warehouses, search and rescue operations, firefighting operations, and maintenance and repairs. It is evident that performing certain tasks, such as moving large and long objects or firefighting operations, is more efficient when robots are deployed cooperatively, and in some cases, these tasks cannot be accomplished by a single robot alone. Therefore, in recent years, the issue of path planning for cooperative robots has received significant attention. By cooperation, we mean that... 

The main goal of this research is to investigate different types of learning methods used on repetitive games. A repetitive game is a model for describing all kinds of frequent activities among humans or intelligent machines. Applying learning models to repeated games is the intersection of game theory and machine learning fields. In the field of game theory, this research is explored under the title of behavioral game theory, where the purpose is to predict the behavior of human beings in repeated games in static environments (without states) and study how they select their actions. In the field of machine learning, repeated games are referred to as multi-agent systems and include problems... 

Buildings account for more than 30% of the world’s total energy consumption. Among building end-uses, air conditioning and in particular cooling systems have a major share of more than 50%. Therefore, design of optimal controllers for AC systems has become increasingly important. Classical and model-free control methods typically lack the ability to optimize energy consumption. On the other hand, model-based optimal control methods rely on precise modeling, which is difficult to acquire due to the complexity of the AC system dynamics.In recent years, deep reinforcement learning has become a popular choice for optimal control of systems with complex dynamics. In this thesis, a deep... 

Nowadays, one of the challenges in social robotics is to navigate the robot in social environments with moving elements such as humans. The purpose of this study is to navigate the Arash 2 social robot in a dynamic environment autonomously without encountering moving obstacles (humans). The Arash 2 robot was first simulated in the Gazebo simulator environment in this research. The simultaneous location and mapping (SLAM) technique was implemented on the robot using a lidar sensor to obtain an environment map. Then, using the deep reinforcement learning approach, the neural network developed in the simulation environment was trained and implemented on the robot in the real environment. The... 

The field of learning from demonstration is the field in which researchers seek to create methods by which a robot can learn and reproduce a skill simply by using the demonstration of the skill. One of the main drawbacks of learning from demonstration methods is their inability to improve the learned skills. To answer this question, the reinforcement learning method can be used. The reinforcement learning approach has the potential to improve the initial skill due to the use of the experience of interacting with the environment. In this project, the dynamic movement primitives algorithm is considered as the learning from demonstration method. The research approach is that first, the dynamic... 

The problem of detection and resolution of aircraft collisions is very important due to the increasing demand for flights. Many algorithms have been developed in the past to increase automation in air traffic management and reduce the workload of air traffic controllers. These algorithms either have difficulty in generalizing to real problems or have high computational costs and do not correspond to the reality of the actual maneuvering characteristics of the aircraft performance. The aim of present study is to obtain dynamic maneuvers that are adaptive with reality and also optimal in terms of utilizing the capacity of flight sectors, so we propose Deep Reinforcement Learning(DRL) based on... 

This study proposes a deep reinforcement learning approach to solve a perishable inventory allocation problem in a two-echelon supply chain. The inventory allocation problem is studied considering the stochastic nature of demand and supply. The examined supply chain includes two retailers and one distribution center (DC) under a vendor-managed inventory (VMI) system. This research aims to minimize the wastages and shortages occurring at the retailer's sites in the examined supply chain. With regard to continuous action space in the considered inventory allocation problem, the Advantage Actor-Critic algorithm is implemented to solve the problem. Numerical experiments are implemented on... 

Price movement prediction has always been one of the traders’ concerns in the field of financial market prediction. In order to increase the profit of the trades, the traders can process the historical data and predict the movement. The large size of the data and complex relations between them lead us to use algorithmic trading and artificial intelligence.The stock and Cryptocurrency markets are two common markets attracting traders. This thesis aims to offer an approach using Twin-Delayed DDPG (TD3) and daily close price in order to achieve a trading strategy. Unlike the previous studies using a discrete action space reinforcement learning algorithm, TD3 is a continuous one offering both... 

Employing wheeled mobile robots is growing in industry, transportation, space and defense industry and many other social fields as well. These robots are used to execute distinct forms of operations and tasks such as exploring the surface of the earth and other planets, serving in public places, backing natural disasters and warehousing, and so forth. In some cases, the assigned mission may not be capable of being performed as intended by a single robot. In this case, several robots will work together to execute a particular mission. Several research topics that are under investigation currently include the interacting procedure of robots as a multi-agent system in order to perform the... 

In the last decade, emergency department (ED) overcrowding has become a national crisis for the US healthcare system. Increasing mortality rates, decreasing quality of care, financial losses due to walkouts, and ambulance diversion are some of the consequences of ED overcrowding. Given the increasing demand in terms of ambulance utilization which we can see an instance of it in the COVID-19 pandemic, being able to allocate service requests to EDs efficiently, becomes a key function of emergency medical services. in this investigation, an algorithm of deep reinforcement learning called deep Q-learning is used to address this problem and to assign ambulances to ED's appropriately. under... 

Over the past few years, deep reinforcement learning(DRL) has been given a lot of attention in finance for portfolio management. With the help of experts’ signals and historical price data, we have developed a new reinforcement learning(RL) method. The use of experts’ signals in tandem with DRL has been used before in finance, but we believe this is the first time this method has been used to solve the financial portfolio management problem. As our agent, we used the Proximal Policy Optimization(PPO) algorithm to process the reward and take actions in the environment. Our framework comprises a convolutional network to aggregate signals, a convolutional network for historical price data, and... 

Image registration is the process of estimating and applying a spatial transformation to a moving image with the aim of spatially aligning it with a fixed image. This allows for the combination of images with complementary information, such as images with different modalities, acquisition times, and even coming from separate individuals, with the purpose of producing more information-rich results. Image registration is a crucial step in many medical applications, such as analyzing the growth and changes of tissue and tumors, preoperative planning, image-guided surgery, radiation therapy planning and various segmentation tasks. Reinforcement learning is a science and mathematical paradigm for... 

Using an efficient method to manage inventory systems is always a challenging issue in supply chain optimization. In supply chains including perishable goods, it is possible to reduce waste and other costs by identifying uncertain demand patterns and managing inventory levels at different stages of the supply chain. Considering the uncertainty and complex conditions of supply chains in the real world, in order to create a suitable model to express these conditions, various uncertain factors must be considered, each of which affects the supply chain inventory level in some way. In this research, a multi-level perishable supply chain model with uncertain demand, lead time and deterioration... 

The aim of this research is to develop and implement a deep reinforcement learning algorithm for portfolio management in the Tehran stock market, which is considered an emerging market with distinct patterns compared to the stock markets of developed countries. In this study, in addition to the market price data extensively used in previous research, we leverage fundamental ratio data extracted from company financial reports, which have received less attention. Furthermore, the research scope is limited to stocks in the mining and metal industries to enable the utilization of specific industry features, such as susceptibility to global prices of a key commodity. The portfolio management... 

In recent years with the progress of technology in different areas, the production of self-driving cars has been feasible. We can expect that vehicles of transportation networks will consist of both self-driving and regular cars in the future. In this research, a new method will be proposed for urban transportation networks to change the direction of reversible lanes according to the network's state. These reversible lanes are exclusive for self-driving cars. Human drivers are not allowed to enter these reversible lanes, considering the limitations of human ability compared to a computer in analyzing data and making decisions about moving direction. To achieve this goal, reinforcement... 

Hybrid electric vehicles (HEV) are proving to be one of the most promising innovations in advanced transportation systems to reduce air pollution and fossil fuel consumption. EMS is one of the most vital aspects of the HEV powertrain system. This research aims to design an optimal EMS under the condition of meeting the goals of drivability control, fuel consumption reduction, and battery charge stability. The current EMS is based on the classical rule-based method derived from fuzzy logic, which guides to the suboptimal solution in episodic driving cycles. Previous experiences in implementing Reinforcement Learning (RL) suffer from late convergence, instability in tracking the driving... 

Deep reinforcement learning has achieved better cumulative rewards than humans in many environments like Atari. One drawback of these methods is their data inefficiency which makes training time-consuming, and in some cases having this amount of data is infeasible. Meta reinforcement learning can use past experiences to enable agents to adapt to new tasks faster and makes neural networks to train in a short amount of time.One of the methods in meta reinforcement learning is inferring tasks which helps exploitation policy to have good performance in new tasks. There’s a need to improve exploration policy as well as exploitation policy by gaining informative transitions about the new task....