Titlebook: Computational and Robotic Models of the Hierarchical Organization of Behavior; Gianluca Baldassarre,Marco Mirolli Book 2013 Springer-Verla

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Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humansr, the simple forms of RL considered in most empirical research do not scale well, making their relevance to complex, real-world behavior unclear. In computational RL, one strategy for addressing the scaling problem is to introduce hierarchical structure, an approach that has intriguing parallels wi

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Neural Network Modelling of Hierarchical Motor Function in the Brainsible neural network architectures with local associative synaptic learning rules. The chapter begins with a review of our own laboratory’s work in this area. We present a series of hierarchical motor models and relate these to various areas of brain function. This is followed by a discussion of the

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https://doi.org/10.1007/978-1-0716-1916-2tion mechanisms, underlying the agent’s ability to add new behaviours to its repertoire. Based on these factors, we review multiple instantiations of a hierarchically-organised biologically-inspired framework for embodied action perception, demonstrating its flexibility in addressing the rich comput

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Jing Xue,Annie Quan,Phillip J. Robinsoned on its highest cognitive layer where knowledge is constructed and used. We investigate the roles of reactive and contextual control depending on the characteristics and complexity of the tasks. We also show how multi-sensor information could be integrated in order to acquire and use knowledge opt