Abstract
In this chapter, we focus on analyzing the truck backer-upper problem (TBU), a real world-like control problem. In this problem a tractor trailer truck must be backed into a specific location with a specific orientation by controlling the orientation of the wheels of the truck cab. We use sequential CART and stochastic kriging to understand how parameters of the neural network and learning algorithm affect convergence and performance in the TBU domain.
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Gatti, C. (2015). The Truck Backer-upper Problem. In: Design of Experiments for Reinforcement Learning. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-12197-0_6
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DOI: https://doi.org/10.1007/978-3-319-12197-0_6
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