Searched for: subject%3A%22reinforcements%22
(1 - 5 of 5)
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Moerland, Thomas M. (author), Broekens, D.J. (author), Plaat, Aske (author), Jonker, C.M. (author)
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have their own research communities. However, if both research fields solve the same problem,...
journal article 2022
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Coppens, Youri (author), Steckelmacher, Denis (author), Jonker, C.M. (author), Nowe, A.S.P. (author)
Today’s advanced Reinforcement Learning algorithms produce black-box policies, that are often difficult to interpret and trust for a person. We introduce a policy distilling algorithm, building on the CN2 rule mining algorithm, that distills the policy into a rule-based decision system. At the core of our approach is the fact that an RL...
conference paper 2021
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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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Jacobs, E.J. (author), Broekens, J. (author), Jonker, C.M. (author)
In this paper we present a mapping between joy, distress, hope and fear, and Reinforcement Learning primitives. Joy / distress is a signal that is derived from the RL update signal, while hope/fear is derived from the utility of the current state. Agent-based simulation experiments replicate psychological and behavioral dynamics of emotion...
conference paper 2014
Searched for: subject%3A%22reinforcements%22
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