Learning state representations with robotic priors

Autonomous Robots

Volumn 39, Issue 3, 2015, Pages 407-428

  Jonschkowski, Rico ^a   Brock, Oliver ^a  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

Prior knowledge; Reinforcement learning; Representation learning; Robot learning

Indexed keywords

ROBOT LEARNING; ROBOTICS; ROBOTS;

HIGH-DIMENSIONAL; LEARNING TASKS; MOVING OBJECTS; PHYSICAL WORLD; PRIOR KNOWLEDGE; REPRESENTATION LEARNING; ROBOTIC EXPERIMENTS; STATE REPRESENTATION;

REINFORCEMENT LEARNING;

EID: 84941943714     PISSN: 09295593     EISSN: 15737527     Source Type: Journal    
DOI: 10.1007/s10514-015-9459-7     Document Type: Article

References (36)

1. Bengio, Y., Courville, A. C., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.
2. Boots, B., Siddiqi, S. M., & Gordon, G. J. (2011). Closing the learning-planning loop with predictive state representations. International Journal of Robotics Research, 30(7), 954–966.
3. Bowling, M., Ghodsi, A., Wilkinson, D. (2005). Action respecting embedding. In 22nd international conference on machine learning (ICML) (pp. 65–72).
4. Cobo, L. C., Subramanian, K., Isbell, C. L, Jr, Lanterman, A. D., & Thomaz, A. L. (2014). Abstraction from demonstration for efficient reinforcement learning in high-dimensional domains. Artificial Intelligence, 216(1), 103–128.
5. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, Koray, & Kuksa, Pavel. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(8), 2493–2537.
6. Duell, S., Udluft, S., & Sterzing, V. (2012). Solving partially observable reinforcement learning problems with recurrent neural networks. In G. Montavon, G. Orr, & K.-R. Müller (Eds.), Neural Networks: Tricks of the trade. Lecture notes in computer science (Vol. 7700, pp. 709–733). Berlin: Heidelberg: Springer.
7. Hadsell, R., Chopra, S., LeCun, Y. (2006). Dimensionality reduction by learning an invariant mapping. In IEEE computer society conference on computer vision and pattern recognition (CVPR) (pp. 1735–1742).
8. Höfer, S., Hild, M., Kubisch, M. (2010). Using slow feature analysis to extract behavioural manifolds related to humanoid robot postures. In 10th international conference on epigenetic robotics (pp. 43–50).
9. Hutter, M. (2009). Feature reinforcement learning: Part I: Unstructured MDPs. Journal of Artificial General Intelligence, 1(1), 3–24.
10. Igel, C., & Hüsken, M. (2003). Empirical evaluation of the improved RPROP learning algorithms. Neurocomputing, 50(1), 105–123.
11. Jenkins, O. C., Matarić, M. J. (2004). A spatio-temporal extension to ISOMAP nonlinear dimension reduction. In 21st international conference on machine learning (ICML) (p. 56).
12. Jetchev, N., Lang, T., Toussaint, M. (2013). Learning grounded relational symbols from continuous data for abstract reasoning. In Autonomous learning workshop at the IEEE international conference on robotics and automation.
13. Jonschkowski, R., Brock, O. (2013). Learning task-specific state representations by maximizing slowness and predictability. In 6th international workshop on evolutionary and reinforcement learning for autonomous robot systems (ERLARS).
14. Jonschkowski, R., & Brock, O. (2014). State representation learning in robotics: Using prior knowledge about physical interaction. In Robotics: Science and Systems (RSS).
15. Kaelbling, L. P., & Littman, M. L. (1998). Planning and acting in partially observable stochastic domains. Artificial intelligence, 101(1), 99–134.
16. Kober, J., Bagnell, J. A., & Peters, J. (2013). Reinforcement learning in robotics: A survey. International Journal of Robotics Research, 32(11), 1238–1274.
17. Konidaris, G., Barto, A. G. (2009). Efficient skill learning using abstraction selection. In 21st international joint conference on artificial intelligence (IJCAI) (pp. 1107–1112).
18. Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (NIPS) (pp. 1106–1114).
19. Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.
20. Lange, S., Riedmiller, M., Voigtländer, A. (2012). Autonomous reinforcement learning on raw visual input data in a real world application. In International joint conference on neural cetworks (IJCNN) (pp. 1–8).
21. Legenstein, R., Wilbert, N., & Wiskott, L. (2010). Reinforcement learning on slow features of high-dimensional input streams. PLoS Computational Biology, 6(8), e1000894.
22. Littman, M. L., Sutton, R. S., Singh, S. (2002). Predictive representations of state. In Advances in neural information processing systems (NIPS) (pp. 1555–1561).
23. Luciw, M., & Schmidhuber, J. (2012). Low complexity proto-value function learning from sensory observations with incremental slow feature analysis. In 22nd international conference on artificial neural networks and machine learning (ICANN) (pp. 279–287).
24. Mahadevan, S., & Maggioni, M. (2007). Proto-value functions: A laplacian framework for learning representation and control in markov decision processes. Journal of Machine Learning Research, 8(10), 2169–2231.
25. Menache, I., Mannor, S., & Shimkin, N. (2005). Basis function adaptation in temporal difference reinforcement learning. Annals of Operations Research, 134(1), 215–238.
26. Piater, J., Jodogne, S., Detry, R., Kraft, D., Krüger, Norbert, Kroemer, Oliver, et al. (2011). Learning visual representations for perception-action systems. International Journal of Robotics Research, 30(3), 294–307.
27. Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.
28. Scholz, J., Levihn, M., Isbell, C., Wingate, D. (2014). A physics-based model prior for object-oriented MDPs. In 31st international conference on machine learning (ICML).
29. Seide, F., Li, G., Yu, D. (2011). Conversational speech transcription using context-dependent deep neural networks. In Interspeech (pp. 437–440).
30. Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237(4820), 1317–1323.
31. Singh, S. P., Jaakkola, T., Jordan, M. I. (1995). Reinforcement learning with soft state aggregation. In Advances in neural information processing systems (NIPS) (pp. 361–368).
32. Sprague, N. (2009). Predictive projections. In 21st international joint conference on artificial intelligence (IJCAI) (pp. 1223–1229).
33. Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An introduction. Cambridge, MA: MIT Press.
34. Tenenbaum, J. B., De Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.
35. van Seijen, H., Whiteson, S., & Kester, L. J. H. M. (2014). Efficient abstraction selection in reinforcement learning. Computational Intelligence, 30(4), 657–699.
36. Wiskott, L., & Sejnowski, T. J. (2002). Slow feature analysis: Unsupervised learning of invariances. Neural Computation, 14(4), 715–770.