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Full-Text Articles in Physical Sciences and Mathematics
Intelligent Traffic Management: From Practical Stochastic Path Planning To Reinforcement Learning Based City-Wide Traffic Optimization, Kamilia Ahmadi
Intelligent Traffic Management: From Practical Stochastic Path Planning To Reinforcement Learning Based City-Wide Traffic Optimization, Kamilia Ahmadi
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
This research focuses on intelligent traffic management including stochastic path planning and city scale traffic optimization. Stochastic path planning focuses on finding paths when edge weights are not fixed and change depending on the time of day/week. Then we focus on minimizing the running time of the overall procedure at query time utilizing precomputation and approximation. The city graph is partitioned into smaller groups of nodes and represented by its exemplar. In query time, source and destination pairs are connected to their respective exemplars and the path between those exemplars is found. After this, we move toward minimizing the city …
Micro Grid Control Optimization With Load And Solar Prediction, Shaju Saha
Micro Grid Control Optimization With Load And Solar Prediction, Shaju Saha
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
Using renewable energy can save money and keep the environment cleaner. Installing a solar PV system is a one-time cost but it can generate energy for a lifetime. Solar PV does not generate carbon emissions while producing power. This thesis evaluates the value of being able to make accurate predictions in the use of solar energy. It uses predicted solar power and load for a system and a battery to store the energy for future use and calculates the operating cost or profit in several designed conditions. Various factors like a different place, tuning the capacity of sources, changing buy/sell …
Deep Q Learning Applied To Stock Trading, Agnibh Dasgupta
Deep Q Learning Applied To Stock Trading, Agnibh Dasgupta
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
Developing a strategy for stock trading is a vital task for investors. However, it is challenging to obtain an optimal strategy, given the complex and dynamic nature of the stock market. This thesis aims to explore the applications of Reinforcement Learning with the goal of maximizing returns from market investment, keeping in mind the human aspect of trading by utilizing stock prices represented as candlestick graphs. Furthermore, the algorithm studies public interest patterns in form of graphs extracted from Google Trends to make predictions. Deep Q learning has been used to train an agent based on fused images of stock …