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General Reinforcement Learning Agents for Crop Management

Theocharis, Thanasis (author)

Agriculture plays a vital role in the global economy, providing the necessary food and resources for human survival. With the world’s population projected to surge, the demand for food is set to escalate in the coming decades. This increasing demand, coupled with the challenges posed by climate change and the detrimental effects of pollution due...

master thesis 2023

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Scripted AI for Overcooked: Designing and Evaluating a Scripted AI Controller for Simplified Overcooked

Anton, Mihai (author)

Overcooked, an immersive multiplayer video game centered around cooperative cooking challenges, provides the roots for this research project. The study focuses on designing and evaluating a hand-authored controller in comparison to controllers implemented using various machine learning techniques, such as Population Based Training, in the...

bachelor thesis 2023

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Getting AI to Cooperate: Sharing a Critic in a Video Game

Groenendijk, Jelle (author)

The popular video game "Overcooked" is a great example of a task requiring complex planning and cooperation with other players. This game is used as the inspiration for an environment for evaluating AI, called "Overcooked-AI". This paper implements a centralized critic into the Overcooked-AI environment's implementation of the PPO algorithm and...

bachelor thesis 2023

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Cooperative AI for Overcooked: Multi-Agent RL with Population-Based Training

Nestorov, Ivan (author)

In ad-hoc cooperative environments, the usage of artificial intelligence to take supportive roles and work in collaboration with humans has proven to be of great benefit. The objective of this research is to evaluate the use of population-based training for reinforcement learning agents in a simplified version of the multiplayer game -...

bachelor thesis 2023

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The Effects of Heuristic Optimisations on Planning Algorithms Within Cooperative AI: Cooperative Planning in Overcooked

Herben, Jonte (author)

Cooperative AI is AI designed to cooperate with humans. One example of such an AI, made using planning algorithms, was studied in a paper from 2019 which used a simplified version of the video game Overcooked for evaluation. However, only limited evaluations were possible due to the long runtime and heuristic optimisations made. This paper will...

bachelor thesis 2023

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Improvements in Imitation Learning for Overcooked

Niemantsverdriet, Duuk (author)

Arguably the main goal of artificial intelligence is to create agents that can collaborate with humans to achieve a shared goal. It has been shown that agents that assume their partner to be optimal can converge to protocols that humans do not understand. Taking human suboptimality into consideration is imperative to perform well in a...

bachelor thesis 2023

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Multi-objective Deep Reinforcement Learning for predictive maintenance of road networks

Krachtopoulos, Konstantinos (author)

Operation and maintenance of the built environment have a major effect on socioeconomic stability and sustainability. A significant part of our built world approaches or has well exceeded its designated structural life. As engineers, we need to find efficient ways to extend this life while maintaining acceptable levels of safety and performance....

master thesis 2023

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Modelling Agents with Variational Autoencoders in Multi-Agent Sequential Decision Making

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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Pacing regulation for runners

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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Multi-Agent Reinforcement Learning using Centralized Critics in Collaborative Environments

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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Fictional Co-Play for Human-Agent Collaboration: Evaluating state-of-the-art reinforcement learning technique for adaptability to human collaborators

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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Smart Team Play: Utility of Population-Based Training for Cooperative AI in Overcooked

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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Coupled and Model-based cooperative planning in Overcooked AI

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

document

Evaluating Design Choices in Tripartite Graph-Based Recommender Systems to Improve Long Tail Recommendations

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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Adapting to Dynamic User Preferences in Recommendation Systems via Deep Reinforcement Learning

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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Alleviating the cold-start problem by using demographic data and domain-aware similarity measure

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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Minimizing the Long-tail Problem in Collaborative Filtering Based Recommender Systems Using Clustering

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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REAL Reinforcement Learning: Planning with adversarial models

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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Aligning AI with Human Norms: Multi-Objective Deep Reinforcement Learning with Active Preference Elicitation

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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Know what it does not know: Improving Offline Deep Reinforcement Learning with Uncertainty Estimation

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