Skip to main content

The Mountain Car Problem

  • 3699 Accesses

Part of the Springer Theses book series (Springer Theses)

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.

Keywords

  • Response Surface
  • Hide Layer
  • Learning Rate
  • Reinforcement Learning
  • Design Point

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-12197-0_5
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-12197-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.99
Price excludes VAT (USA)
Hardcover Book
USD   179.99
Price excludes VAT (USA)
Fig. 5.1
Fig. 5.2
Fig. 5.3
Fig. 5.4
Fig. 5.5
Fig. 5.6
Fig. 5.7
Fig. 5.8
Fig. 5.9

References

  • Embrechts, M. J., Hargis, B. J., & Linton, J. D. (2010). An augmented efficient backpropagation training strategy for deep autoassociative neural networks. In Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 18–23 July (pp. 1–6). doi: 10.1109/IJCNN.2010. 5596828

    Google Scholar 

  • Gatti, C. J., Embrechts, M. J., & Linton, J. D. (2013). An empirical analysis of reinforcement learning using design of experiments. In Proceedings of the \(21st\) European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 24–26 April (pp. 221–226). Bruges, Belgium: ESANN.

    Google Scholar 

  • LeCun,Y., Bottou, L., Orr, G.,, & Müller, K. (1998). Efficient backprop. In Orr, G. & Müller, K. (Eds.), Neural Networks: Tricks of the Trade, volume 1524 (pp. 5–50). Berlin: Springer.

    Google Scholar 

  • Moore, A. W. (1990). Efficient memory-based learning for robot control. Unpublished PhD dissertation, University of Cambridge, Cambridge, United Kingdom.

    Google Scholar 

  • Patist, J. P. & Wiering, M. (2004). Learning to play draughts using temporal difference learning with neural networks and databases. In Proceedings of the 13th Belgian-Dutch Conference on Machine Learning, Brussels, Belgium, 8–9 January (pp. 87–94). doi: 10.1007/978-3-540-88190-2_13

    Google Scholar 

  • Tesauro, G. (1992). Practical issues in temporal difference learning. Machine Learning, 8(3–4), 257–277.

    Google Scholar 

  • Wiering, M. A. (2010). Self-play and using an expert to learn to play backgammon with temporal difference learning. Journal of Intelligent Learning Systems & Applications, 2(2), 57–68.

    CrossRef  Google Scholar 

  • Wiering, M. A., Patist, J. P., & Mannen, H. (2007). Learning to play board games using temporal difference methods (Technical Report UU–CS–2005–048, Institute of Information and Computing Sciences, Utrecht University). Retrieved from http://www.ai.rug.nl/\(\sim \)http://www.ai.rug.nl/ mwiering/group/articles/learning_games_TR.pdf

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christopher Gatti .

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12197-0_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12196-3

  • Online ISBN: 978-3-319-12197-0

  • eBook Packages: EngineeringEngineering (R0)