Searched for: contributor%3A%22Oliehoek%2C+F.A.+%28mentor%29%22
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Lenferink, Luc (author)
The ability to model other agents can be of great value in multi-agent sequential decision making problems and has become more accessible due to the introduction of deep learning into reinforcement learning. In this study, the aim is to investigate the usefulness of modelling other agents using variational autoencoder based models in partially...
master thesis 2023
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Molano Valencia, Juan Esteban (author)
By increasing the step frequency of the runners, it is possible to reduce the risk of injuries due to overload. Techniques like auditory pacing help the athletes to have better control over their step frequency. Nevertheless, synchronizing to a continuous external rhythm costs energy. For this reason, the use of intermittent pacing may be more...
master thesis 2022
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van Veen, Nils (author)
In the field of cooperative AI, an environment is created called Overcooked AI based on the popular Overcooked game. Originally the environment is used to study deep reinforcement learning, on the other hand it also allows for cooperative planning methods of which the paper will focus on. These methods include coupled based planning with...
bachelor thesis 2022
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Mija, Andrei (author)
Agents trained through single-agent reinforcement learning methods such as self-play can provide a good level of performance in multi-agent settings and even in fully cooperative environments. However, most of the time, training multiple agents together using single-agent self-play yields poor results as each agent tries to learn how to perform...
bachelor thesis 2022
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Ordonez Cardenas, Nathan (author)
A longstanding problem in the area of reinforcement learning is human-agent col- laboration. As past research indicates that RL agents undergo a distributional shift when they start collaborating with human beings, the goal is to create agents that can adapt. We build upon research using the two-player Overcooked environment to repro- duce a...
bachelor thesis 2022
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Moreira-Kanaley, Janaína (author)
In an ad-hoc teamwork environment, artificial intelligence agents have the potential to take on supportive roles and complete tasks in collaboration with human players. The following paper investigates the use of employing population-based training (PBT) for reinforcement learning agents in the multi-player game Overcooked. In addition to this,...
bachelor thesis 2022
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Crul, Thomas (author)
Even though the abaility to recommend items in the long tail is one of the main strengths of recommendation systems, modern models still show decreased performance when recommending these niche items. Various bipartite and tripartite graph-based models have been proposed that are specifically tailored to solving this long tail issue. This study...
bachelor thesis 2022
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Mundhra, Yash (author)
Recommender systems are an essential part of online businesses in today's day and age. They provide users with meaningful recommendations for items and products. A frequently occurring problem in recommender systems is known as the long-tail problem. It refers to a situation in which a majority of the items in the data set have limited ratings...
bachelor thesis 2022
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Pantea, Luca (author)
Recommender Systems play a significant part in filtering and efficiently prioritizing relevant information to alleviate the information overload problem and maximize user engagement. Traditional recommender systems employ a static approach towards learning the user's preferences, relying on logged previous interactions with the system,...
bachelor thesis 2022
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Kalaria, Rahul (author)
Recommender systems (RS) are a cornerstone for most online businesses that cater to a large customer base such as e-commerce, social network platforms and many others. RS's enable these platforms to provide tailor-made experiences to each of their customers by strategically utilizing users/items rating data or any other available data....
bachelor thesis 2022
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Foffano, Daniele (author)
Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usually formalised as Markov Decision Processes, using a model of the environment dynamics to compute the optimal policy. When dealing with complex environments, the environment dynamics are frequently approximated with function approximators (such as...
master thesis 2022
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Peschl, Markus (author)
The field of deep reinforcement learning has seen major successes recently, achieving superhuman performance in discrete games such as Go and the Atari domain, as well as astounding results in continuous robot locomotion tasks. However, the correct specification of human intentions in a reward function is highly challenging, which is why state...
master thesis 2021
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Smit, Jordi (author)
Offline reinforcement learning, or learning from a fixed data set, is an attractive alternative to online reinforcement learning. Offline reinforcement learning promises to address the cost and safety implications of taking numerous random or bad actions online, which is a crucial aspect of traditional reinforcement learning that makes it...
master thesis 2021
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Fani, Kees (author)
In order to do something useful, you should know what to do and how to do it. The same goes for robots or other machines, which are also referred to as agents. Advances in e.g. technology and science have made such agents more and more sophisticated and capable. This opens up a plethora of possibilities for solving problems or automating...
master thesis 2021
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Inna Kedege, Vibhav (author)
Distributed cooperative robots can be highly beneficial in mapping disaster environments and assisting with search and rescue operations. In most situations such environments only allow for only limited communication between robots. This thesis reports on simulation experiments conducted to test the impact of having only partial communication...
master thesis 2021
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Pool, Lourens (author)
Optimization of traffic signal control has been widely investigated by means of model-based strategies. In 2012 a new model-based controller was published, named Schedule-driven Intersection Control (SCHIC). This controller uses a job-scheduling algorithm to minimize the cumulative delay for all observed vehicles. The algorithms of SCHIC are at...
master thesis 2021
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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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Li, Mingxi (author)
Neural networks have achieved great success in many difficult learning tasks like image classification, speech recognition and natural language processing. However, neural architectures are hard to design, which requires lots of knowledge and time of human experts. Therefore, there has been a growing interest in automating the process of designing...
master thesis 2019
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Voorhout, Damian (author)
Using some sort of adaptive traffic light control system is becoming standard policy among metropolitan areas. However, controlling traffic lights efficiently on a city-wide scale is computationally intensive and theoretically complex. This paper aims to show a proof of concept of an efficient and modular traffic. light controller with...
bachelor thesis 2019
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