Abstract
The mountain car problem is commonly used as a benchmark reinforcement learning problem to evaluate learning algorithms. The problem places a car in a valley, where the goal is to get the car to drive out of the valley (Fig. 5.1). The car’s engine is not powerful enough for it to drive out of the valley, and the car must instead build up momentum by successively driving up opposing sides of the valley. This chapter explores the mountain car problem using sequential CART and stochastic kriging to understand the parameter space.
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Gatti, C. (2015). The Mountain Car Problem. In: Design of Experiments for Reinforcement Learning. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-12197-0_5
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DOI: https://doi.org/10.1007/978-3-319-12197-0_5
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