Abstract
Two main practical problems arise when dealing with autonomous learning of the control of Linked Multi-Component Robotic Systems (L-MCRS) with Reinforcement Learning (RL): time and space consumption, due to the convergence conditions of the RL algorithm applied, i.e. Q-Learning algorithm, and the complexity of the system model. Model approximate response allows to speedup the realization of RL experiments. We have used a multivariate regression approximation model based on Artificial Neural Networks (ANN), which has achieved a 90% and 27% of time and space savings compared to the conventional Geometrically Exact Dynamic Splines (GEDS) model.
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Lopez-Guede, J.M., Graña, M. (2015). Neural Modeling of Hose Dynamics to Speedup Reinforcement Learning Experiments. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_33
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DOI: https://doi.org/10.1007/978-3-319-18833-1_33
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