Searched for: subject:"reinforcement%5C+learning"
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Cornet, R. (author)
Fully automated vehicles have the potential to increase road safety and improve traffic flow by taking the human element out of the driving loop. They can also provide mobility to people who are unable to operate a conventional vehicle. Safe automated vehicles must be able to respond in emergency situations or drive on slippery roads in bad...
master thesis 2020
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Schneider, C.C.N. (author)
Signalized urban intersections are bottlenecks for traffic and cause congestion. To improve traffic signal plans, research efforts have been made to create self-adaptive traffic controllers, i.e. controllers which adapt in real-time to the current traffic demand based on connected vehicle data. Past research on self-adaptive controllers has...
master thesis 2020
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Kosiorek, A. (author)
Steel production is a complex problem, and little has been done to improve it with the usage of Reinforcement Learning techniques. Most studies focus on decomposing it into sub-problems, instead of tacking it as a whole. Research has shown promising results in the area of safe policy improvement on toy problems. These algorithms are not only...
master thesis 2020
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Subramanian, S.M. (author)
The de-carbonisation of the energy system, more commonly known as the 'Energy Transition' has a vital role to play in the pursuit of mitigating the climate emergency’s impact. There is a global trend of moving toward making power systems more future-proof and this largely affects the roles and activities of a Transmission System Operator (TSO)...
master thesis 2020
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Kovács, B. (author)
Deep reinforcement learning went through an unprecedented development in the last decade, resulting in agents defeating world champion human players in complex board games like go and chess. With few exceptions, deep reinforcement learning research focuses on fully observable environments, while there is slightly less research in the direction...
master thesis 2020
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Mandersloot, A.V. (author)
The Decentralized Partially Observable Markov Decision Process is a commonly used framework to formally model scenarios in which multiple agents must collaborate using local information. A key difficulty in a Dec-POMDP is that in order to coordinate successfully, an agent must decide on actions not only using its own information, but also by...
master thesis 2020
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Albers, Nele (author)
We analyze the internal representations that deep Reinforcement Learning (RL) agents form of their environments and whether these representations correspond to what such agents should ideally learn. The purpose of this comparison is both a better understanding of why certain algorithms or network architectures perform better than others and the...
master thesis 2020
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Koomen, Lenard (author)
The combination of reinforcement learning and deep neural networks has the potential to train intelligent autonomous agents on high dimensional sensory inputs, with applications in flight control. However, the amount of samples needed by these methods is often too large to use real-world interaction. In this work, mirror-descent guided policy...
master thesis 2020
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Oren, Yaniv (author)
Identifying the most efficient exploration approach for deep reinforcement learning in traffic light control is not a trivial task, and can be a critical step in the development of reinforcement learning solutions that can effectively reduce traffic congestion. It is common to use baseline dithering methods such as $\epsilon$-greedy. However,...
bachelor thesis 2020
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van der Toorn, Eric (author)
A recent advancement in Reinforcement Learning is the capability of modelling opponents. In this work, we are interested in going back to basics and testing this capability within the Iterated Prisoner's Dilemma, a simple method for modelling multi agent systems. Using the self modelling advantage actor critic model, we set up a single agent...
bachelor thesis 2020
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Jansen, Cian (author)
Traffic congestion is a problem of tremendous size that affects many people. Using Reinforcement Learning to find a light control policy can ease traffic congestion and decrease travel time for vehicles. This paper specifically looks at the effect of using different reward functions for training agents. We highlight how the learnabilty of a...
bachelor thesis 2020
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Koning, Tim (author)
Reinforcement Learning (RL) is a learning paradigm where an agent learns a task by trial and error. The agent needs to explore its environment and by simultaneously receiving rewards it learns what is appropriate behaviour.<br/>Even though it has roots in machine learning, RL is essentially different from other machine learning methods. In...
master thesis 2020
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This work addresses the problem of exploration and coverage using visual inputs. Exploration and coverage is a fundamental problem in mobile robotics, the goal of which is to explore an unknown environment in order to gain vital information. Some of the diverse scenarios and applications in which exploratory robots can have a significant impact...
master thesis 2020
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Gupta, Sukrit (author)
A lot research has been conducted in the field of autonomous navigation of mobile robots with focus on Robot Vision and Robot Motion Planning. However, most of the classical navigation solutions require several steps of data pre-processing and hand tuning of parameters, with separate modules for vision, localization, planning and control. All...
master thesis 2020
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Fris, Rein (author)
Deep Reinforcement Learning (DRL) enables us to design controllers for complex tasks with a deep learning approach. It allows us to design controllers that are otherwise cumbersome to design with conventional control methodologies. Often, an objective for RL is binary in nature. However, exploring in environments with sparse rewards is a problem...
master thesis 2020
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Konatala, Ramesh (author)
Online Adaptive Flight Control is interesting in the context of growing complexity of aircraft systems and their adaptability requirements to ensure safety. An Incremental Approximate Dynamic Programming (iADP) controller combines reinforcement learning methods, optimal control and Online identified incremental model to achieve optimal adaptive...
master thesis 2020
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Samad, Azlaan Mustafa (author)
In today’s scenario due to rapid urbanisation there has been a shift of population from rural to urban areas especially in developing countries in search of better opportunities. This has lead to unprecedented growth of cities leading to various urbanisation problems. One of the main problems that comes across in urban areas is the increased...
master thesis 2020
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Scavuzzo Montana, Lara (author)
Mixed Integer Linear Programming (MILP) is a generalization of classical linear programming where we restrict some (or all) variables to take integer values. Numerous real-world problems can be modeled as MILPs, such as production planning, scheduling, network design optimization and many more. MILPs are, in fact, NP-hard. State-of-the-art...
master thesis 2020
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de Bruin, T.D. (author)
The arrival of intelligent, general-purpose robots that can learn to perform new tasks autonomously has been promised for a long time now. Deep reinforcement learning, which combines reinforcement learning with deep neural network function approximation, has the potential to enable robots to learn to perform a wide range of new tasks while...
doctoral thesis 2020
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Martens, Vera (author)
A Taxi Dispatch Problem involves assigning taxis to requests of passengers who are waiting at different locations for a trip. In today's economy and society, the Taxi Dispatch Problem and other transport problems can be found everywhere. Not only in transporting people, but also in food delivery from restaurants and package delivery for all kind...
bachelor thesis 2020
Searched for: subject:"reinforcement%5C+learning"
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