Abstract
The autonomous learning of the control of Linked Multicomponent Robotic Systems (L-MCRS) is an open research issue. We are pursuing the application of Reinforcement Learning algorithms to achieve such control. However, accurate simulations needed for RL trials are time consuming, so that the process of training and validation becomes excesively long. In order to obtain results in affordable time, we perform the approximation of the detailed dynamic model of the L-MCRS by Artificial Neural Networks (ANN).
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Lopez-Guede, J.M., Graña, M., Ramos-Hernanz, J.A., Oterino, F. (2013). A Neural Network Approximation of L-MCRS Dynamics for Reinforcement Learning Experiments. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Computation in Engineering and Medical Applications. IWINAC 2013. Lecture Notes in Computer Science, vol 7931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38622-0_33
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DOI: https://doi.org/10.1007/978-3-642-38622-0_33
Publisher Name: Springer, Berlin, Heidelberg
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