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Multi-expert Preference Alignment in Reinforcement Learning

Li, LITIAN (author)

This project explores adaptation to preference shifts in Multi-objective Reinforcement Learning (MORL), with a focus on how Reinforcement Learning (RL) agents can align with the preferences of multiple experts. This alignment can occur across various scenarios featuring distinct preferences of experts or within a single scenario that experiences...

master thesis 2024

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Preference-Based Reinforcement Learninig in Demand Response Programs

Piccini, Pietro (author)

ncentive-based demand response (iDR) programs serve as important tools for distributed system operators (DSOs) to achieve a reduction in electricity demand during periods of grid overload. During these programs, participants can decide to curtail their consumption in exchange for financial incentives. Deciding the amount of curtailment for a...

master thesis 2024

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Counterfactual Explanations of Learned Reward Functions

Wehner, Jan (author)

Learning rewards from humans is a promising approach to aligning AI with human values. However, methods are not able to consistently extract the correct reward functions from demonstrations or feedback. To allow humans to understand the limitations and misalignments of a learned reward function we adopt the technique of counterfactual...

master thesis 2023

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Investigating the extent to which inverse reinforcement learning can learn Rrewards from noisy demonstrations

Perdikis, Charalampos (author)

Inverse Reinforcement Learning (IRL) aims to recover a reward function from expert demonstrations in a Markov Decision Process (MDP). The objective is to understand the underlying intentions and behaviors of experts and derive a reward function based on their reasoning, rather than their exact actions. However, expert demonstrations can be...

bachelor thesis 2023

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Inverse Reinforcement Learning (IRL) in Presence of Risk and Uncertainty Related Cognitive Biases: To what extent can IRL learn rewards from expert demonstrations with loss and risk aversion?

Ikiz, Meric (author)

A key issue in Reinforcement Learning (RL) research is the difficulty of defining rewards. Inverse Reinforcement Learning (IRL) is a technique that addresses this challenge by learning the rewards from expert demonstrations. In a realistic setting, expert demonstrations are collected from humans, and it is important to acknowledge that these...

bachelor thesis 2023

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What are the implications of Curriculum Learning strategy on IRL methods?: Investigating Inverse Reinforcement Learning from Human Behavior

Vlasenko, Mikhail (author)

Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on recovering the reward function using expert demonstrations. In the field of IRL, Adversarial IRL (AIRL) is a promising algorithm that is postulated to recover non-linear rewards in environments with unknown dynamics. This study investigates the...

bachelor thesis 2023

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Conflicting demonstrations in Inverse Reinforcement Learning

Labbé, Rafael (author)

This paper aims to investigate the effect of conflicting demonstrations on Inverse Reinforcement Learning (IRL). IRL is a method to understand the intent of an expert, by only feeding it demonstrations of that expert, which may be a promising approach for areas such as self driving vehicles, where there are a lot of demonstrations from experts....

bachelor thesis 2023

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Aggregation of energy consumption forecasts across spatial levels: Using CNN-LSTM forecasts of lower spatial levels to forecast on higher spatial levels

Borst, Twan (author)

Bottom up load forecasting, is a technique where energy consumption forecasts are made on lower spatial levels, after which the resulting forecasts are aggregated to form forecasts of higher spatial levels. With the current move to renewable energy sources and the importance of reducing the strain on an already congested electricity grid,...

bachelor thesis 2023

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Aggregation and Prediction of Energy Consumption Data: What is the Aggregatino Level at which a Graph Neural Network Performs Optimally?

Timp, Lennard (author)

Electrical load forecasting, namely short-term load forecasting, is essential to power grids’ safe and efficient operations. The need for accurate short-term load forecasting becomes increasingly pressing with increased renewable energy sources, which are stochastic in their power supply. Most forecasting models are focused on the temporal...

bachelor thesis 2023

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Improving the Generalisability of Deep Learning NILM Algorithms using One-Shot Transfer Learning: Can one-shot transfer learning be leveraged to enhance the performance of a CNN-based NILM algorithm on unseen data?

Verschuren, Wim (author)

Non-Intrusive Load Monitoring (NILM) is a technique used to disaggregate household power consumption data into individual appliance components without the need for dedicated meters for each appliance. This paper focuses on improving the generalizability of NILM algorithms to unseen households using Convolutional Neural Networks (CNNs) and one...

bachelor thesis 2023

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Disaggregation of Community Level Energy Data to Individual Households: Using sequence-to-point learning

Verlaan, Stan (author)

Non-intrusive load monitoring (NILM) is a well-researched concept that aims to provide insights into individual appliance energy usage without the need for dedicated meters. This paper explores the possibility of applying the NILM concept to disaggregate energy data from a community level to a household level. By doing so, it addresses privacy...

bachelor thesis 2023

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Mechanism to detect mismatches and provide recommendations about the users' preference in negotiation support systems: A case study about issue weight mismatches with the pocket negotiator

Xuan, Yuhao (author)

Negotiation Support Systems (NSSs) can provide help based on the preference setting (domain, issue weights, issue ranking, strategies, etc.) of the users of the systems. However, sometimes the users of the systems might make mistakes in the preference setting. With wrong preferences, the NSSs might provide suggestions that conflict with the...

master thesis 2023

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Elucidating a ‘black-box’ transcends explaining the algorithm: Exploring Explainable AI (XAI) as a way to address AI implementation challenges in the Dutch public sector

Rajiv, Miny (author)

Responding to the trend of increasing use of artificial intelligence (AI), we need to ensure applications of AI are designed, implemented, utilised and evaluated in a careful manner. Explainable AI, or XAI, meaning; - given a certain audience, the details and reasons of both technical processes of the algorithm-support system and the reasoning...

master thesis 2023

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Opponent Modeling in Automated Negotiation Applied to P2P Energy Trading

Xia, Lichen (author)

Automated negotiation is a key form of interaction in systems composed of multiple autonomous agents with different preferences. Such interactions aim to reach agreements through an iterative process of making offers. With the growth of Peer-to-Peer (P2P) energy markets due to the development and deployment of a variety of small-scale...

master thesis 2022

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Investigation of the fairness metrics in automated negotiations

Rubio Bizcaino, Andrzej (author)

This paper aims to define the broad concept of fairness and investigate how it can be measured, especially considering fairness in automated negotiations. The report relies on the work on fairness issues that have been derived from the research of C. Albin [1]. Firstly, the paper elaborates on different fairness metrics from the literature...

bachelor thesis 2022

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BOFA - A Framework for Fairness in Automated Negotiations

Blagoev, Nikolay (author)

As automated negotiating agents become more and more part of our daily life, additional care needs to be taken that the agents can negotiate fairly. Humans each have their own intrinsic view on fairness, which affects the negotiation processes and the degree to which the outcome is viewed as satisfactory. However, most current agents are built...

bachelor thesis 2022

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Integrating fairness in opponent modeling

Trestioreanu, Ilinca (author)

Is there a way to incorporate fairness in the opponent modeling component of an automated agent? Since opponent modeling plays an important role in a negotiation strategy, it is reasonable to research how fairness can be integrated into this component, as it influences the outcome of the negotiation. A first step towards finding an answer to...

bachelor thesis 2022

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The Unintended Consequences Fairness Brings to Automated Negotiation

Ouwerkerk, Nick (author)

In this paper, the unintended consequences, also named edge cases in this paper, of integrating fairness into the automated negotiation process are researched. By finding these unintended consequences, we can deal with them accordingly or avoid them, as to not cause any problems with our fairness metric that might make our negotiation process...

bachelor thesis 2022

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Fairness by Discussion: An Alternative View on the Fairness of Protocols in Automated Negotiation

Cochavi, Zohar (author)

The field of automated negotiation promises to improve negotiations, thus, a fair outcome and process should also be considered when building these systems. However, issues exist with computational approaches to fairness with which the field of computer science is mainly concerned. To this end, we propose a new approach to fairness based on that...

bachelor thesis 2022

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MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response

van Tilburg, Jasper (author)

Distribution System Operators (DSOs) are responsible preventing grid congestion, while accounting for growing demand and the intermittent nature of renewable energy resources. Incentive-based demand response programs promise real-time flexibility to relieve grid congestion. To include residential consumers in these programs, aggregators can...

master thesis 2021