Essays about: "deep q-learning"
Showing result 16 - 20 of 55 essays containing the words deep q-learning.
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16. Edge Case searching for Autonomous Vehicles
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : In this thesis, a method that generates critical situations for autonomous vehicles with fewer resources was investigated. To produce these key examples, the strategy in this thesis explores optimization and reinforcement learning. These key examples are defined as Edge cases; these cases are situations on the border between safe and unsafe. READ MORE
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17. Board Game AI Using Reinforcement Learning
University essay from Örebro universitet/Institutionen för naturvetenskap och teknikAbstract : The purpose of this thesis is to develop an agent that learns to play an interpretation ofthe popular game Ticket To Ride. This project is done in collaboration with Piktiv AB.This thesis presents how an agent based on the Double Deep Q-network algorithm learnsto play a version of Ticket To Ride using self-play. READ MORE
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18. Risk Averse Path Planning Using Lipschitz Approximated Wasserstein Distributionally Robust Deep Q-Learning
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : We investigate the problem of risk averse robot path planning using the deep reinforcement learning and distributionally robust optimization perspectives. Our problem formulation involves modelling the robot as a stochastic linear dynamical system, assuming that a collection of process noise samples is available. READ MORE
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19. Deep Reinforcement Learning for Building Control : A comparative study for applying Deep Reinforcement Learning to Building Energy Management
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Energy and environment have become hot topics in the world. The building sector accounts for a high proportion of energy consumption, with over one-third of energy use globally. A variety of optimization methods have been proposed for building energy management, which are mainly divided into two types: model-based and model-free. READ MORE
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20. Application of Deep Q-learning for Vision Control on Atari Environments
University essay from Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisationAbstract : The success of Reinforcement Learning (RL) has mostly been in artificial domains, with only some successful real-world applications. One of the reasons being that most real-world domains fail to satisfy a set of assumptions of RL theory. READ MORE
