Computational and Robotic Models of the Hierarchical by Gianluca Baldassarre, Marco Mirolli

By Gianluca Baldassarre, Marco Mirolli

Current robots and different man made structures tend to be capable of accomplish just one unmarried job. Overcoming this quandary calls for the advance of regulate architectures and studying algorithms that may aid the purchase and deployment of numerous varied talents, which in flip turns out to require a modular and hierarchical association. during this approach, diversified modules can gather various talents with no catastrophic interference, and higher-level elements of the approach can clear up complicated initiatives by means of exploiting the talents encapsulated within the lower-level modules. whereas computer studying and robotics realize the basic value of the hierarchical association of habit for development robots that scale as much as clear up advanced projects, study in psychology and neuroscience exhibits expanding facts that modularity and hierarchy are pivotal association ideas of habit and of the mind. they could even bring about the cumulative acquisition of an ever-increasing variety of talents, which appears a attribute of mammals, and people in particular.

This ebook is a entire evaluation of the cutting-edge at the modeling of the hierarchical association of habit in animals, and on its exploitation in robotic controllers. The e-book standpoint is very interdisciplinary, that includes versions belonging to all correct components, together with laptop studying, robotics, neural networks, and computational modeling in psychology and neuroscience. The ebook chapters assessment the authors' newest contributions to the research of hierarchical habit, and spotlight the open questions and such a lot promising examine instructions. because the contributing authors are one of the pioneers undertaking basic paintings in this subject, the booklet covers an important and topical concerns within the box from a computationally trained, theoretically orientated viewpoint. The e-book should be of gain to educational and commercial researchers and graduate scholars in comparable disciplines.

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2002). Automated state abstraction for options using the U-tree algorithm. In T. G. Dietterich, S. Becker, Z. ), Advances in neural information processing systems 14: proceedings of the 2001 neural information processing systems (NIPS) conference (pp. 1054–1060). Cambridge: MIT. , & Barto, A. G. (2006). Causal graph based decomposition of factored mdps. Journal of Machine Learning Research, 7, 2259–2301. , & Barto, A. G. (2007). Active learning of dynamic Bayesian networks in Markov decision processes.

The right kinds of skills—based on the right kinds of representations and abstractions—can make an agent “competent” (White 1959) in an environment, meaning that it is able to efficiently solve many different problems that can come up in that environment. Beyond this, competence can extend from multiple tasks in a single environment to multiple tasks in multiple environments, where the environments have features in common that make it possible to transfer knowledge and skills from one to another.

Chentanez, N. (2004). Intrinsically motivated learning of hierarchical collections of skills. In J. Triesch & T. ), Proceedings of the 2004 international conference on development and learning (pp. 112–119). UCSD Institute for Neural Computation. Barto, A. G. (2012). Intrinsic motivation and reinforcement learning. In G. Baldassarre & M. ), Intrinsically motivated learning in natural and artificial system. Berlin: Springer. Barto, A. , & Mahadevan, S. (2003). Recent advances in hierarchical reinforcement learning.

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