Searched for: subject:"Reinforcement%5C+Learning"
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de Nijs, F. (author)
Intelligent autonomous agents, designed to automate and simplify many aspects of our society, will increasingly be required to also interact with other agents autonomously. Where agents interact, they are likely to encounter resource constraints. For example, agents managing household appliances to optimize electricity usage might need to share...
doctoral thesis 2019
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Pane, Yudha P. (author), Nageshrao, Subramanya P. (author), Kober, J. (author), Babuska, R. (author)
Smart robotics will be a core feature while migrating from Industry 3.0 (i.e., mass manufacturing) to Industry 4.0 (i.e., customized or social manufacturing). A key characteristic of a smart system is its ability to learn. For smart manufacturing, this means incorporating learning capabilities into the current fixed, repetitive, task-oriented...
journal article 2019
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Junell, J. (author)
The use of Micro Aerial Vehicles (MAVs) in practical applications, to solve real-world problems, is growing in demand as the technology becomes more widely known and accessible. Proposed applications already span a wide berth of fields like military, search and rescue, ecology, artificial pollinators, and more. As compared to larger Unmanned...
doctoral thesis 2018
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Koryakovskiy, I. (author)
Reinforcement learning is an active research area in the fields of artificial intelligence and machine learning, with applications in control. The most important feature of reinforcement learning is its ability to learn without prior knowledge about the system. However, in the real world, reinforcement learning actions may lead to serious damage...
doctoral thesis 2018
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Zhou, Y. (author)
Reinforcement Learning (RL) methods are relatively new in the field of aerospace guidance, navigation, and control. This dissertation aims to exploit RL methods to improve the autonomy and online learning of aerospace systems with respect to the a priori unknown system and environment, dynamical uncertainties, and partial observability. In the...
doctoral thesis 2018
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Mannucci, T. (author), van Kampen, E. (author), de Visser, C.C. (author), Chu, Q. (author)
Self-learning approaches, such as reinforcement learning, offer new possibilities for autonomous control of uncertain or time-varying systems. However, exploring an unknown environment under limited prediction capabilities is a challenge for a learning agent. If the environment is dangerous, free exploration can result in physical damage or in...
journal article 2018
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Li, Guangliang (author), Whiteson, Shimon (author), Bradley Knox, W (author), Hung, H.S. (author)
Learning from rewards generated by a human trainer observing an agent in action has been proven to be a powerful method for teaching autonomous agents to perform challenging tasks, especially for those non-technical users. Since the efficacy of this approach depends critically on the reward the trainer provides, we consider how the...
journal article 2018
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Moerland, T.M. (author), Broekens, D.J. (author), Jonker, C.M. (author)
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are...
journal article 2018
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Calli, B. (author), Caarls, W. (author), Wisse, M. (author), Jonker, P.P. (author)
Grasp synthesis for unknown objects is a challenging problem as the algorithms are expected to cope with missing object shape information. This missing information is a function of the vision sensor viewpoint. The majority of the grasp synthesis algorithms in literature synthesize a grasp by using one single image of the target object and...
journal article 2018
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de Bruin, T.D. (author), Kober, J. (author), Tuyls, K.P. (author), Babuska, R. (author)
Experience replay is a technique that allows off-policy reinforcement-learning methods to reuse past experiences. The stability and speed of convergence of reinforcement learning, as well as the eventual performance of the learned policy, are strongly dependent on the experiences being replayed. Which experiences are replayed depends on two...
journal article 2018
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Leottau, David L. (author), Ruiz-del-Solar, Javier (author), Babuska, R. (author)
A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual behaviors in problems where multi-dimensional action spaces are involved. When using this methodology, sub-tasks are learned in parallel by individual agents working toward a common goal. In addition to proposing this methodology, three specific...
journal article 2018
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Celemin Paez, C.E. (author), Ruiz-del-Solar, Javier (author), Kober, J. (author)
Reinforcement Learning agents can be supported by feedback from human teachers in the learning loop that guides the learning process. In this work we propose two hybrid strategies of Policy Search Reinforcement Learning and Interactive Machine Learning that benefit from both sources of information, the cost function and the human corrective...
journal article 2018
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Alibekov, Eduard (author), Kubalik, Jiri (author), Babuska, R. (author)
This paper addresses the problem of deriving a policy from the value function in the context of critic-only reinforcement learning (RL) in continuous state and action spaces. With continuous-valued states, RL algorithms have to rely on a numerical approximator to represent the value function. Numerical approximation due to its nature virtually...
journal article 2018
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Mannucci, T. (author)
doctoral thesis 2017
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Oliehoek, Frans A. (author), Spaan, M.T.J. (author), Terwijn, Bas (author), Robbel, Philipp (author), Messias, João V. (author)
This article describes the MultiAgent Decision Process (MADP) toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in uncertain environments. Key features are that it supports partially observable environments and stochastic transition models; has unified support for single- and multiagent...
journal article 2017
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Wang, C. (author)
doctoral thesis 2017
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Koryakovskiy, I. (author), Kudruss, M. (author), Babuska, R. (author), Caarls, W. (author), Kirches, Christian (author), Mombaur, Katja (author), Schlöder, Johannes P. (author), Vallery, H. (author)
Model-free reinforcement learning and nonlinear model predictive control are two different approaches for controlling a dynamic system in an optimal way according to a prescribed cost function. Reinforcement learning acquires a control policy through exploratory interaction with the system, while nonlinear model predictive control exploits an...
journal article 2017
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Koryakovskiy, I. (author), Vallery, H. (author), Babuska, R. (author), Caarls, W. (author)
Reinforcement learning techniques enable robots to deal with their own dynamics and with unknown environments without using explicit models or preprogrammed behaviors. However, reinforcement learning relies on intrinsically risky exploration, which is often damaging for physical systems. In the case of the bipedal walking robot Leo, which is...
conference paper 2017
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Kubalìk, Jiřì (author), Alibekov, Eduard (author), Babuska, R. (author)
Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper addresses the problem of finding a smooth policy...
conference paper 2017
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Najafi, E. (author)
Sequential composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers sequentially to achieve a control specification that cannot be realized by a single controller. Sequential composition focuses on the interaction between a collection of pre-designed...
doctoral thesis 2016
Searched for: subject:"Reinforcement%5C+Learning"
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