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A Q-learning based multi-strategy integrated artificial bee colony algorithm with application in unmanned vehicle path planning

Ni, Xinrui (author), Hu, Wei (author), Fan, Q. (author), Cui, Yibing (author), Qi, Chongkai (author)

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm ...

journal article 2024

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A Method for Embodied Co-Learning in Interdependent Human-Robot Teams

Loopik, Hugo (author)

This paper addresses the research question: “How can a human-robot team achieve co-learning, and interdependence in physically embodied tasks?”<br/>A method has been developed that enables a human-robot team to co-learn the handover of an object from the robot to the human. Five design requirements were composed to address the challenges of...

master thesis 2023

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ClimAIte Control: Improving Building Operation Through AI

Stripp, Sebastian (author)

Current building operations can be improved through smart predictive operation based on weather and use patterns in order to save energy with minimal impact on the building fabric and daily use. The existing literature has investigated implementations, and potential savings through combining with variable tariffs, however, this thesis addresses...

master thesis 2023

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On the road from Model-Based Dynamical Programming to Model-Free Reinforcement Learning: A sample efficient approach

Mur Uribe, Pol (author)

This thesis introduces a new method, called Mixed Iteration, for controlling Markov Decision Processes when partial information is known about the dynamics of the Markov Decision Process. The algorithm uses sampling to calculate the expectation of partially known dynamics in stochastic environments. Its goal is to lower the number of iterations...

master thesis 2023

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Optimal Energy Scheduling of Flexible Industrial Prosumers via Reinforcement Learning

van den Bovenkamp, Nick (author), Giraldo, Juan S. (author), Salazar Duque, Edgar Mauricio (author), Vergara Barrios, P.P. (author), Konstantinou, Charalambos (author), Palensky, P. (author)

This paper introduces an energy management system (EMS) aiming to minimize electricity operating costs using reinforcement learning (RL) with a linear function approximation. The proposed EMS uses a Q-learning with tile coding (QLTC) algorithm and is compared to a deterministic mixed-integer linear programming (MILP) with perfect forecast...

conference paper 2023

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Distributed Actor-Critic Algorithms for Multiagent Reinforcement Learning Over Directed Graphs

Dai, Pengcheng (author), Yu, Wenwu (author), Wang, He (author), Baldi, S. (author)

Actor-critic (AC) cooperative multiagent reinforcement learning (MARL) over directed graphs is studied in this article. The goal of the agents in MARL is to maximize the globally averaged return in a distributed way, i.e., each agent can only exchange information with its neighboring agents. AC methods proposed in the literature require the...

journal article 2023

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Improved DQN-Based Computation Offloading Algorithm in MEC Environment

Zhao, Zheyu (author), Cheng, H. (author), Xu, Xiaohua (author)

Massive terminal users have brought explosive need of data residing at edge of overall network. Multiple Mobile Edge Computing (MEC) servers are built in/near base station to meet this need. However, optimal distribution of these servers to multiple users in real time is still a problem. Reinforcement Learning (RL) as a framework to solve...

conference paper 2023

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DMQL: Deep Maximum Q-Learning: Combatting Relative Overgeneralisation in Deep Independent Learners using Optimism and Similarity

Dam, Erwin (author)

Various pathologies can occur when independent learners are used in cooperative Multi-Agent Reinforcement Learning. One such pathology is Relative Overgeneralisation, which manifests when a suboptimal Nash Equilibrium in the joint action space of a problem is preferred over an optimal Equilibrium. Approaches exist to combat relative...

master thesis 2022

document

Sequential decision making using Bayesian networks and reinforcement learning: to determine the hydrocarbon prospect exploration strategy

Veerhoek, Laura (author)

In the process of drilling wells to produce hydrocarbons, an exploration strategy is used to determine which wells should be drilled and in which order. This strategy is vital, as a suboptimal drilling sequence will lead to more expenses and fewer gains.<br/>Furthermore, the wells considered in most exploration strategies are geologically<br/...

master thesis 2022

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The Feasibility of Reinforcement Learning as a replacement for Mixed-Integer Programming for Smart Charging of Electric Vehicles

Becker, Simion (author)

As the world is currently actively trying to reduce the consumption of fossil fuels, large investments are done in renewable energy sources and ways are sought after to electrify fossil fuel-intensive sectors. In line with these developments, the number of electric vehicles requiring access to the electric power grid has exploded putting...

master thesis 2022

document

Predictive Maintenance Decisions for a Multi-Component Aircraft based on Prognostics

Posthuma, Luuk (author)

Aircraft maintenance is critical to an airline's operations to ensure the reliability, availability, and safety of their assets. Recently, the approach of using component prognostics in aircraft maintenance has received increasing attention in academic- and industrial research. Predictive maintenance has demonstrated promising results in using...

master thesis 2022

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Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning

Stepanovic, K. (author), Wu, J. (author), Everhardt, Rob (author), de Weerdt, M.M. (author)

abstract 2022

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Event-Based Communication in Distributed Q-Learning

Jarne Ornia, D. (author), Mazo, M. (author)

We present an approach to reduce the communication of information needed on a Distributed Q-Learning system inspired by Event Triggered Control (ETC) techniques. We consider a baseline scenario of a Distributed Q-Learning problem on a Markov Decision Process (MDP). Following an event-based approach, N agents sharing a value function explore the...

conference paper 2022

document

Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning

Stepanovic, K. (author), Wu, J. (author), Everhardt, Rob (author), de Weerdt, M.M. (author)

The integration of pipeline energy storage in the control of a district heating system can lead to profit gain, for example by adjusting the electricity production of a combined heat and power (CHP) unit to the fluctuating electricity price. The uncertainty from the environment, the computational complexity of an accurate model, and the scarcity...

journal article 2022

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Global synchromodal shipment matching problem with dynamic and stochastic travel times: a reinforcement learning approach

Guo, W. (author), Atasoy, B. (author), Negenborn, R.R. (author)

Global synchromodal transportation involves the movement of container shipments between inland terminals located in different continents using ships, barges, trains, trucks, or any combination among them through integrated planning at a network level. One of the challenges faced by global operators is the matching of accepted shipments with...

journal article 2022

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Simulating an Scheduling Airport Security Checkpoints: Q-Learning-Based Allocation of Operators to Security Teams at an Airport Security Checkpoint

Köstler, Klemens (author)

In this paper, we propose and analyze a q-learning-based approach for allocation of operators to security teams in order to improve operational efficiency of an airport security checkpoint. The research is composed of two parts. First, we develop an agent-based model capable of simulating an airport security checkpoint. Second, we introduce...

master thesis 2021

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Deep Reinforcement Learning for Rapid Communication Network Recovery with Multiple Repair Crews

Vilhjálmsson, Ingimundur (author)

Natural disasters can destroy communication network components, potentially leading to severe losses in connectivity. During those devastating events, network connectivity is crucial for rescue teams as well as anyone in need of assistance. Therefore, swift network restoration following a disaster is vital. However, post-disaster network...

master thesis 2021

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Reinforcement learning for order distribution in self-organizing logistics

de Vries, Yorick (author)

With the increasing global demand for logistics, supply chains have grown a lot in volume over the last decades. To be able to operate effectively within the capacity constraints of the carriers, proper collaboration and optimization of order allocation is required. Van Berkel Logistics facilitates the transport of containers by trucks from sea...

master thesis 2021

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Comparing Model-Free Deep Reinforcement Learning Algorithms on Stock Market

Meral, Murat Kaan Meral (author)

Automated asset trading is a crucial method used by financial entities such as investment firms or hedge funds. It allows them to allocate their capital in order to maximize their rate of returns. In scientific literature, there are multiple models suggested to solve this problem. However, these models either lack the complexity to understand...

bachelor thesis 2021

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Comparing Deep Reinforcement Learning Approaches for Sparse Reward Settings with Discrete State-Action Spaces

Çapanoğlu, Alp (author)

One of the most challenging types of environments for a Deep Reinforcement Learning agent to learn in are those with sparse reward functions. There exist algorithms that are designed to perform well in settings with sparse rewards, but they are often applied to continuous state-action spaces, since economically relevant problems like robotic...

bachelor thesis 2021