Q-Learning Algorithm Module in Hybrid Artificial Neural Network Systems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00217982" target="_blank" >RIV/68407700:21230/14:00217982 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007/978-3-319-06740-7_11" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-06740-7_11</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-06740-7_11" target="_blank" >10.1007/978-3-319-06740-7_11</a>

Result language
angličtina
Original language name
Q-Learning Algorithm Module in Hybrid Artificial Neural Network Systems
Original language description
Presented topic is from the research field called Artificial Life, but contributes also to the field of Artificial Intelligence (AI), Robotics and potentially into many other aspects of research. In this paper, there is reviewed and tested new approach to autonomous design of agent architectures. This novel approach is inspired by inherited modularity of biological brains. During designing of new brains, the evolution is not directly connecting individual neurons. Rather than that, it composes new brains by connecting larger, widely reused areas (modules). In this approach, agent architectures are represented as hybrid artificial neural networks composed of heterogeneous modules. Each module can implement different selected algorithm. Rather than describing this framework, this paper focuses on designing of one module. Such a module represents one component of hybrid neural network and can seamlessly integrate a selected algorithm into the node. The course of design of such a module is
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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