Essays about: "MDP"
Showing result 1 - 5 of 13 essays containing the word MDP.
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1. DECISION-MAKING FOR AUTONOMOUS CONSTRUCTION VEHICLES
University essay from Mälardalens högskola/Inbyggda system; Mälardalens högskola/Inbyggda systemAbstract : Autonomous driving requires tactical decision-making while navigating in a dynamic shared space environment. The complexity and uncertainty in this process arise due to unknown and tightly-coupled interaction among traffic users. READ MORE
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2. Privacy-Preserving Sharing of Health Data using Hybrid Anonymisation Techniques : A Comparison
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Data anonymisation is not a trivial task due to the challenge of balancing the trade-off between anonymity and data utility. A fairly new attempt to address this challenge is the development of hybrid anonymisation algorithms a combination of syntactic privacy models, often k-anonymity, and differential privacy. READ MORE
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3. User Plane Selection for Core Networks using Deep Reinforcement Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Allocating service functions to a core network upon users’ various demands isof importance in 5G networks. In this thesis work, we have studied reinforcementlearning models to solve this allocation problem. READ MORE
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4. Using Markov Decision Processes and Reinforcement Learning to Guide Penetration Testers in the Search for Web Vulnerabilities
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Bug bounties are an increasingly popular way of performing penetration tests of web applications. User statistics of bug bounty platforms show that a lot of hackers struggle to find bugs. READ MORE
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5. Minimal Exploration in Episodic Reinforcement Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Exploration-exploitation trade-off is a fundamental dilemma that reinforcement learning algorithms face. This dilemma is also central to the design of various state of the art bandit algorithms. We take inspiration from these algorithms and try to design reinforcement learning algorithms in an episodic setting. READ MORE
