Searched for: subject%3A%22Reinforcement%255C+Learning%255C+%255C%2528RL%255C%2529%22
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Berjaoui Tahmaz, Amin (author)
This paper presents a hierarchical reinforcement learning framework for efficient robotic manipulation in sequential contact tasks. We leverage this hierarchical structure to sequentially execute behavior primitives with variable stiffness control capabilities for contact tasks. Our proposed approach relies on three key components: an action...
master thesis 2024
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Molhoek, Jord (author)
Many real-world problems fall in the category of sequential decision-making under uncertainty; Markov Decision Processes (MDPs) are a common method for modeling such problems. To solve an MDP, one could start from scratch or one could already have an idea of what good policies look like. Furthermore, there could be uncertainty in this idea. In...
master thesis 2024
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Ştefan, Andrei (author)
Physical activity is one of the main factors that contribute to reducing the chance of chronic diseases such as cardiovascular disease, obesity, and depression, all while improving an individual’s health in general. While this is the case, the fact still remains that many adults across the world do not reach the minimum recommendations for...
master thesis 2023
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ATHREY, ARCHITH (author)
This thesis addresses the Learning-Based Control (LBC) of unknown partially observable systems in the Linear Quadratic (LQ) paradigm. In this setting of learning-based LQ control, the control action influences not only the control performance but also the rate at which the system is being learnt, causing a conflict between learning and control ...
master thesis 2023
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Lubbers, Seymour (author)
Greenhouses allow production of crops that would otherwise be impossible. Permitting more local, fresher and nutrient richer crop production. Eorts are taken to minimize societal harm due to energy and resource consumption by greenhouse production systems. One way to control such systems is by using model predictive control. Optimal crop yield...
master thesis 2023
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Vieira dos Santos, Lucas (author)
The critical challenge for employing autonomous control systems in aircraft is ensuring robustness and safety. This study introduces an intelligent and fault-tolerant controller that merges two Reinforcement Learning (RL) algorithms in a hybrid approach: the Distributional Soft Actor-Critic (DSAC) and the Incremental Dual Heuristic Programming ...
master thesis 2023
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Herren Aguillar van de Laar, Thomas (author)
master thesis 2023
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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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Yeh, Jefferson (author)
Wind energy, generated by windfarms, is playing an increasingly critical role in meeting current and future energy demands. windfarms, however, face a challenge due to the inherent flaw of wake-induced power losses when turbines are located in close proximity. Wakes, characterized by regions of turbulence and lower wind speed, are created as air...
bachelor thesis 2023
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Ortal, Bartu (author)
This research paper aims to investigate the effect of entropy while training the agent on the robustness of the agent. This is important because robustness is defined as the agent's adaptability to different environments. A self-driving car should adapt to every environment that it is being used in since a mistake could cost someone's life....
bachelor thesis 2023
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Sözüdüz, Mehmet (author)
Reinforcement Learning (RL) has gained atten-tion as a way of creating autonomous agents for self-driving cars. This paper explores the adap- tation of the Deep Q Network (DQN), a popular deep RL algorithm, in the Carla traffic simulator for autonomous driving. It investigates the influ- ence of action space discretization and DQN ex-<br/...
bachelor thesis 2023
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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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Seres, Peter (author)
With the recent increase in the complexity of aerospace systems and autonomous operations, there is a need for an increased level of adaptability and model-free controller synthesis. Such operations require the controller to maintain safety and performance without human intervention in non-static environments with partial observability and...
master thesis 2022
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Casals Sadlier, Juliette (author)
The implementation of a model-free, off-policy, actor-critic deep reinforcement learning algorithm consistent of two separate agents to a six-degree-of freedom spacecraft docking maneuver to develop a control policy is carried out in the research presented in this article. Reinforcement learning has the ability to learn without instruction, this...
master thesis 2022
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Wijnands, Patrick (author)
This thesis has provided insight into how machine learning can be beneficial to path planning in container terminals. Path planning algorithms can be used in environments with automated vehicles. A well known algorithm is the A* path planning algorithm, which is the fastest optimal path planning algorithm under satisfied conditions. However, the...
master thesis 2022
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Fledderus, Eddy (author)
The domains of the negotiation can vary significantly. It is possible that a domain is very cooperative, where both agents can receive a high utility; the opposite is also possible, where the domain is very competitive and the agents cannot both get a high utility. In the same manner, the agents can have different strategies leading to a...
bachelor thesis 2022
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van Zijl, Job (author)
Deep Reinforcement Learning (DRL) shows great potential for flight control, due to its adaptability, fault-tolerance, and as it does not require an accurate system model. However, these techniques, like many machine learning applications, are considered black-box as their inner workings are hidden. This paper aims to break open the black box of...
master thesis 2022
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Meijer, Caspar (author)
Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning,...
bachelor thesis 2022
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Suau, M. (author), He, J. (author), Spaan, M.T.J. (author), Oliehoek, F.A. (author)
Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and the pace at which that data can be obtained. In this paper, we study how to build lightweight simulators of complicated systems that can run sufficiently fast for...
conference paper 2022
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Zhou, Y. (author)
Globalized dual heuristic programming (GDHP) is the most comprehensive adaptive critic design, which employs its critic to minimize the error with respect to both the cost-to-go and its derivatives simultaneously. Its implementation, however, confronts a dilemma of either introducing more computational load by explicitly calculating the...
journal article 2022
Searched for: subject%3A%22Reinforcement%255C+Learning%255C+%255C%2528RL%255C%2529%22
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