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Full-Text Articles in Physical Sciences and Mathematics
Scheduling Allocation And Inventory Replenishment Problems Under Uncertainty: Applications In Managing Electric Vehicle And Drone Battery Swap Stations, Amin Asadi
Graduate Theses and Dissertations
In this dissertation, motivated by electric vehicle (EV) and drone application growth, we propose novel optimization problems and solution techniques for managing the operations at EV and drone battery swap stations. In Chapter 2, we introduce a novel class of stochastic scheduling allocation and inventory replenishment problems (SAIRP), which determines the recharging, discharging, and replacement decisions at a swap station over time to maximize the expected total profit. We use Markov Decision Process (MDP) to model SAIRPs facing uncertain demands, varying costs, and battery degradation. Considering battery degradation is crucial as it relaxes the assumption that charging/discharging batteries do not …
Improving Asynchronous Advantage Actor Critic With A More Intelligent Exploration Strategy, James B. Holliday
Improving Asynchronous Advantage Actor Critic With A More Intelligent Exploration Strategy, James B. Holliday
Graduate Theses and Dissertations
We propose a simple and efficient modification to the Asynchronous Advantage Actor Critic (A3C)
algorithm that improves training. In 2016 Google’s DeepMind set a new standard for state-of-theart
reinforcement learning performance with the introduction of the A3C algorithm. The goal of
this research is to show that A3C can be improved by the use of a new novel exploration strategy we
call “Follow then Forage Exploration” (FFE). FFE forces the agents to follow the best known path
at the beginning of a training episode and then later in the episode the agent is forced to “forage”
and explores randomly. In …