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Functions and Mechanisms of Intrinsic Motivations

The Knowledge Versus Competence Distinction

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Intrinsically Motivated Learning in Natural and Artificial Systems

Abstract

Mammals, and humans in particular, are endowed with an exceptional capacity for cumulative learning. This capacity crucially depends on the presence of intrinsic motivations, that is, motivations that are directly related not to an organism’s survival and reproduction but rather to its ability to learn. Recently, there have been a number of attempts to model and reproduce intrinsic motivations in artificial systems. Different kinds of intrinsic motivations have been proposed both in psychology and in machine learning and robotics: some are based on the knowledge of the learning system, while others are based on its competence. In this contribution, we discuss the distinction between knowledge-based and competence-based intrinsic motivations with respect to both the functional roles that motivations play in learning and the mechanisms by which those functions are implemented. In particular, after arguing that the principal function of intrinsic motivations consists in allowing the development of a repertoire of skills (rather than of knowledge), we suggest that at least two different sub-functions can be identified: (a) discovering which skills might be acquired and (b) deciding which skill to train when. We propose that in biological organisms, knowledge-based intrinsic motivation mechanisms might implement the former function, whereas competence-based mechanisms might underlie the latter one.

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References

  1. Alexander, G., DeLong, M., Strick, P.: Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, 357–381 (1986)

    Google Scholar 

  2. Baldassarre, G.: A modular neural-network model of the basal ganglia’s role in learning and selecting motor behaviours. J. Cogn. Syst. Res. 3, 5–13 (2002a)

    Google Scholar 

  3. Baldassarre, G.: Planning with neural networks and reinforcement learning. Ph.D. Thesis, Computer Science Department, University of Essex (2002b)

    Google Scholar 

  4. Baldassarre, G., Mirolli, M.: What are the key open challenges for understanding autonomous cumulative learning of skills? AMD Newslett. 7(2), 2–3 (2010)

    Google Scholar 

  5. Baldassarre, G., Mirolli, M.: Deciding which skill to learn when: Temporal-difference competence-based intrinsic motivation (td-cb-im). In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)

    Google Scholar 

  6. Baranes, A., Oudeyer, P.-Y.: R-iac: Robust intrinsically motivated exploration and active learning. IEEE Trans. Auton. Mental Dev. 1(3), 155–169 (2009)

    Google Scholar 

  7. Baranes, A., Oudeyer, P.-Y.: Intrinsically motivated goal exploration for active motor learning in robots: A case study. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS 2010). Taipel, Taiwan (2010)

    Google Scholar 

  8. Barto, A.: Adaptive critics and the basal ganglia. In: Houk, J.C., Davis, J., Beiser, D. (eds.) Models of Information Processing in the Basal Ganglia, pp. 215–232. MIT, Cambridge (1995)

    Google Scholar 

  9. Barto, A., Singh, S., Chentanez, N.: Intrinsically motivated learning of hierarchical collections of skills. In: International Conference on Developmental Learning (ICDL), La Jolla (2004)

    Google Scholar 

  10. Barto, A., Sutton, R., Anderson, C.: Neuron-like adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern. 13, 834–846 (1983)

    Google Scholar 

  11. Barto, A.G.: What are intrinsic reward signals? AMD Newslett. 7(2), 3 (2010)

    MathSciNet  Google Scholar 

  12. Barto, A.G.: Intrinsic motivation and reinforcement learning. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)

    Google Scholar 

  13. Barto, A.G., Mahadevan, S.: Recent advances in hierarchical reinforcement learning. Discr. Event Dyn. Syst. 13(4), 341–379 (2003)

    MathSciNet  MATH  Google Scholar 

  14. Berlyne, D.E.: Conflict, Arousal., Curiosity. McGraw-Hill, New York (1960)

    Google Scholar 

  15. Botvinick, M., Plaut, D.: Doing without schema hierarchies: A recurrent connectionist approach to routine sequential action and its pathologies. Psychol. Rev. 111, 395–429 (2004)

    Google Scholar 

  16. Brooks, R.A.: Intelligence without representation. Artif. Intell. J. 47, 139–159 (1991)

    Google Scholar 

  17. Butler, R.A.: Discrimination learning by rhesus monkeys to visual-exploration motivation. J. Comp. Physiol. Psychol. 46(2), 95–98 (1953)

    Google Scholar 

  18. Caligiore, D., Mirolli, M., Parisi, D., Baldassarre, G.: A bioinspired hierarchical reinforcement learning architecture for modeling learning of multiple skills with continuous states and actions. In: Proceedings of the Tenth International Conference on Epigenetic Robotics, vol. 149. Lund University Cognitive Studies, Lund (2010)

    Google Scholar 

  19. Clark, A.: Being There: Putting Brain, Body and World Together Again. Oxford University Press, Oxford (1997)

    Google Scholar 

  20. Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper Perennial, New York (1991)

    Google Scholar 

  21. Dalley, J.W., Cardinal, R.N., Robbins, T.W.: Prefrontal executive and cognitive functions in rodents: Neural and neurochemical substrates. Neurosci. Biobehav. Rev. 28(7), 771–784 (2004)

    Google Scholar 

  22. Dayan, P., Hinton, G.E.: Feudal reinforcement learning. In: Advances in Neural Information Processing Systems 5, pp. 271–278. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  23. De Charms, R.: Personal Causation: The Internal Affective Determinants of Behavior. Academic, New York (1968)

    Google Scholar 

  24. Deci, E.: Intrinsic Motivation. Plenum, New York (1975)

    Google Scholar 

  25. Deci, E.L., Ryan, R.M.: Intrinsic Motivation and Self-determination in Human Behavior. Plenum, New York (1985)

    Google Scholar 

  26. Dember, W., Earl, R.: Analysis of exploratory, manipulatory and curiosity behaviors. Psychol. Rev. 64, 91–96 (1957)

    Google Scholar 

  27. Dietterich, T.: Hierarchical reinforcement learning with the maxq value function decomposition. J. Artif. Intell. Res. 13, 227–303 (2000)

    MathSciNet  MATH  Google Scholar 

  28. Dommett, E., Coizet, V., Blaha, C.D., Martindale, J., Lefebvre, V., Walton, N., Mayhew, J.E.W., Overton, P.G., Redgrave, P.: How visual stimuli activate dopaminergic neurons at short latency. Science 307(5714), 1476–1479 (2005)

    Google Scholar 

  29. Doya, K.: Complementary roles of basal ganglia and cerebellum in learning and motor control. Curr. Opin. Neurobiol. 10(6), 732–739 (2000)

    Google Scholar 

  30. Doya, K., Samejima, K., Katagiri, K.-i., Kawato, M.: Multiple model-based reinforcement learning. Neural Comput. 14(6), 1347–1369 (2002)

    MATH  Google Scholar 

  31. Festinger, L.: A Theory of Cognitive Dissonance. Stanford University Press, Stanford (1957)

    Google Scholar 

  32. Fiore, V., Mannella, F., Mirolli, M., Gurney, K., Baldassarre, G.: Instrumental conditioning driven by neutral stimuli: A model tested with a simulated robotic rat. In: Proceedings of the Eight International Conference on Epigenetic Robotics, number 139, pp. 13–20. Lund University Cognitive Studies, Lund (2008)

    Google Scholar 

  33. Fuster, J.: The prefrontal cortex-an update: Time is of the essence. Neuron 2, 319–333 (2001)

    Google Scholar 

  34. Geisler, S., Derst, C., Veh, R.W., Zahm, D.S.: Glutamatergic afferents of the ventral tegmental area in the rat. J. Neurosci. 27(21), 5730–5743 (2007)

    Google Scholar 

  35. Grafton, S.T., Hamilton, A.: Evidence for a distributed hierarchy of action representation in the brain. Hum. Brain Mapp. Movement Sci. 26(4), 590–616 (2007)

    Google Scholar 

  36. Graziano, M.: The organization of behavioral repertoire in motor cortex. Annu. Rev. Neurosci. 29, 105–134 (2006)

    Google Scholar 

  37. Gurney, K., Lepora, N., Shah, A., Koene, A., Redgrave, P.: Action discovery and intrinsic motivation: A biologically constrained formalisation. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)

    Google Scholar 

  38. Gurney, K., Prescott, T.J., Redgrave, P.: A computational model of action selection in the basal ganglia I. A new functional anatomy. Biol. Cybern. 84(6), 401–410 (2001)

    MATH  Google Scholar 

  39. Harlow, H.F.: Learning and satiation of response in intrinsically motivated complex puzzle performance by monkeys. J. Comp. Physiol. Psychol. 43, 289–294 (1950)

    Google Scholar 

  40. Harlow, H.F., Harlow, M.K., Meyer, D.R.: Learning motivated by a manipulation drive. J. Exp. Psychol. 40, 228–234 (1950)

    Google Scholar 

  41. Haruno, M., Wolpert, D., Kawato, M.: Mosaic model for sensorimotor learning and control. Neural Comput. 13, 2201–2220 (2001)

    MATH  Google Scholar 

  42. Hebb, D.: Drives and the conceptual nervous system. Psychol. Rev. 62, 243–254 (1955)

    Google Scholar 

  43. Heidbreder, C.A., Groenewegen, H.J.: The medial prefrontal cortex in the rat: Evidence for a dorso-ventral distinction based upon functional and anatomical characteristics. Neurosci. Biobehav. Rev. 27(6), 555–579 (2003)

    Google Scholar 

  44. Hof, P.M., Scherer, C., Heuberger, P.S. (eds.): Model-Based Control: Bridging Rigorous Theory and Advanced Technology. Springer, Berlin (2009)

    Google Scholar 

  45. Horvitz, J.: Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events. Neuroscience 96(4), 651–656 (2009)

    Google Scholar 

  46. Houk, J., Adams, J., Barto, A.: A model of how the basal ganglia generates and uses neural signals that predict reinforcement. In: Houk, J., Davis, J., Beiser, D. (eds.) Models of Information Processing in the Basal Ganglia, pp. 249–270. MIT, Cambridge (1995)

    Google Scholar 

  47. Huang, X., Weng, J.: Novelty and reinforcement learning in the value system of developmental robots. In: Proceedings Second International Workshop on Epigenetic Robotics, Edinburgh, pp. 47–55 (2002)

    Google Scholar 

  48. Hull, C.L.: Principles of Behavior. Appleton-Century-Crofts, New York (1943)

    Google Scholar 

  49. Hunt, H.: Intrinsic motivation and its role in psychological development. Nebraska Symp. Motiv. 13, 189–282 (1965)

    Google Scholar 

  50. Joel, D., Niv, Y., Ruppin, E.: Actor-critic models of the basal ganglia: New anatomical and computational perspectives. Neural Netw. 15(4), 535–547 (2002)

    Google Scholar 

  51. Joel, D., Weiner, I.: The organization of the basal ganglia-thalamocortical circuits: Open interconnected rather than closed segregated. Neuroscience 63(2), 363–379 (1994)

    MathSciNet  Google Scholar 

  52. Jonsson, A., Barto, A.: Causal graph based decomposition of factored mdps. J. Mach. Learn. Res. 7, 2259–2301 (2006)

    MathSciNet  MATH  Google Scholar 

  53. Jordan, M.I., Rumelhart, D.E.: Forward models: Supervised learning with a distal teacher. Cogn. Sci. 16, 307–354 (1992)

    Google Scholar 

  54. Kagan, J.: Motives and development. J. Pers. Soc. Psychol. 22, 51–66 (1972)

    Google Scholar 

  55. Kish, G.: Learning when the onset of illumination is used as the reinforcing stimulus. J. Comp. Physiol. Psychol. 48(4), 261–264 (1955)

    Google Scholar 

  56. Kish, G., Antonitis, J.: Unconditioned operant behavior in two homozygous strains of mice. J. Genet. Psychol. Aging 88(1), 121–129 (1956)

    Google Scholar 

  57. Konidaris, G.D., Barto, A.G.: Skill discovery in continuous reinforcement learning domains using skill chaining. In: Advances in Neural Information Processing Systems (NIPS 2009), pp. 1015–1023. Vancouver, B.C., Canada (2009)

    Google Scholar 

  58. Langton, C.G. (ed.): Artificial Life: The Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems. Addison-Wesley, Redwood City (1989)

    Google Scholar 

  59. Lee, R., Walker, R., Meeden, L., Marshall, J.: Category-based intrinsic motivation. In: Proceedings of the Ninth International Conference on Epigenetic Robotics, vol. 146, pp. 81–88. Lund University Cognitive Studies, Lund (2009)

    Google Scholar 

  60. Lisman, J.E., Grace, A.A.: The hippocampal-vta loop: Controlling the entry of information into long-term memory. Neuron 46(5), 703–713 (2005)

    Google Scholar 

  61. Marshall, J., Blank, D., Meeden, L.: An emergent framework for self-motivation in developmental robotics. In: Proceedings of the Third International Conference on Development and Learning (ICDL 2004), La Jolla, pp. 104–111 (2004)

    Google Scholar 

  62. Merrick, K., Maher, M.L.: Motivated learning from interesting events: Adaptive, multitask learning agents for complex environments. Adap. Behav. 17(1), 7–27 (2009)

    Google Scholar 

  63. Meunier, D., Lambiotte, R., Bullmore, E.T.: Modular and hierarchically modular organization of brain networks. Front. Neurosci. 4 (2010)

    Google Scholar 

  64. Meyer, J.-A., Wilson, S.W. (eds.): From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior. MIT, Cambridge (1990)

    Google Scholar 

  65. Miller, E., Cohen, J.: An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001)

    Google Scholar 

  66. Mink, J.: The basal ganglia: Focused selection and inhibition of competing motor programs. Prog. Neurobiol. 50(4), 381–425 (1996)

    Google Scholar 

  67. Mirolli, M., Santucci, V.G., Baldassarre, G.: Phasic dopamine as a prediction error of intrinsic and extrinsic reinforcements driving both action acquisition and reward maximization: A simulated robotic study. Neural Netw. (2012, submitted for publication)

    Google Scholar 

  68. Mitchell, T.M.: Mach. Learn.. McGraw-Hill, New York (1997)

    Google Scholar 

  69. Montgomery, K.: The role of exploratory drive in learning. J. Comp. Physiol. Psychol. 47, 60–64 (1954)

    Google Scholar 

  70. Otmakova, N., Duzel, E., Deutch, A.Y., Lisman, J.E.: The hippocampal-vta loop: The role of novelty and motivation in controlling the entry of information into long-term memory. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)

    Google Scholar 

  71. Oudeyer, P.-Y., Kaplan, F.: What is intrinsic motivation? A typology of computational approaches. Front. Neurorobot. (2007)

    Google Scholar 

  72. Oudeyer, P.-Y., Kaplan, F., Hafner, V.V.: Intrinsic motivation systems for autonomous mental development. IEEE Trans. Evol. Comput. 11(2), 265–286 (2007)

    Google Scholar 

  73. Parr, R., Russell, S.J.: Reinforcement learning with hierarchies of machines. In: Advances in Neural Information Processing Systems. MIT, Cambridge (1997)

    Google Scholar 

  74. Pfeifer, R., Scheier, C.: Understanding intelligence. MIT, Cambridge (1999)

    Google Scholar 

  75. Provost, J., Kuipers, B.J., Miikkulainen, R.: Developing navigation behavior through self-organizing distinctive state abstraction. Connect. Sci. 18(2), 159–172 (2006)

    Google Scholar 

  76. Redgrave, P.: Basal ganglia. Scholarpedia 2(6), 1825 (2007)

    Google Scholar 

  77. Redgrave, P., Gurney, K.: The short-latency dopamine signal: A role in discovering novel actions? Nat. Rev. Neurosci. 7(12), 967–975 (2006)

    Google Scholar 

  78. Redgrave, P., Gurney, K., Stafford, T., Thirkettle, M., Lewis, J.: The role of the basal ganglia in discovering novel actions. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)

    Google Scholar 

  79. Redgrave, P., Prescott, T., Gurney, K.: The basal ganglia: A vertebrate solution to the selection problem? Neuroscience 89, 1009–1023 (1999)

    Google Scholar 

  80. Reed, P., Mitchell, C., Nokes, T.: Intrinsic reinforcing properties of putatively neutral stimuli in an instrumental two-lever discrimination task. Anim. Learn. Behav. 24, 38–45 (1996)

    Google Scholar 

  81. Reynolds, J.N., Wickens, J.R.: Dopamine-dependent plasticity of corticostriatal synapses. Neural Netw. 15(4–6), 507–521 (2002)

    Google Scholar 

  82. Rizzolatti, G., Luppino, G.: The cortical motor system. Neuron 31(6), 889–901 (2001)

    Google Scholar 

  83. Romanelli, P., Esposito, V., Schaal, D.W., Heit, G.: Somatotopy in the basal ganglia: Experimental and clinical evidence for segregated sensorimotor channels. Brain Res. Rev. 48, 112–28 (2005)

    Google Scholar 

  84. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Upper Saddle River (2003)

    MATH  Google Scholar 

  85. Ryan, R., Deci, E.: Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemp. Educ. Psychol. 25, 54–67 (2000)

    Google Scholar 

  86. Santucci, V., Baldassarre, G., Mirolli, M.: Biological cumulative learning requires intrinsic motivations: A simulated robotic study on the development of visually-guided reaching. In: Proceedings of the Tenth International Conference on Epigenetic Robotics, vol. 149. Lund University Cognitive Studies, Lund (2010)

    Google Scholar 

  87. Saunders, R., Gero, J.: Curious agents and situated design evaluations. In: Gero, J., Brazier, F. (eds.) Agents in Design 2002, pp. 133–149. Key Centre of Design Computing and Cognition, University of Sydney, Sydney (2002)

    Google Scholar 

  88. Schembri, M., Mirolli, M., Baldassarre, G.: Evolution and learning in an intrinsically motivated reinforcement learning robot. In: Advances in Artificial Life. Proceedings of the 9th European Conference on Artificial Life, LNAI, vol. 4648, pp. 294–333. Springer, Berlin (2007a)

    Google Scholar 

  89. Schembri, M., Mirolli, M., Baldassarre, G.: Evolving childhood’s length and learning parameters in an intrinsically motivated reinforcement learning robot. In: Proceedings of the Seventh International Conference on Epigenetic Robotics, pp. 141–148. Lund University Cognitive Studies, Lund (2007b)

    Google Scholar 

  90. Schembri, M., Mirolli, M., Baldassarre, G.: Evolving internal reinforcers for an intrinsically motivated reinforcement-learning robot. In: Proceedings of the 6th International Conference on Development and Learning, pp. E1–E6. Imperial College, London (2007c)

    Google Scholar 

  91. Schmidhuber, J.: Curious model-building control systems. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1458–1463. IEEE, Singapore (1991a)

    Google Scholar 

  92. Schmidhuber, J.: A possibility for implementing curiosity and boredom in model-building neural controllers. In: From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior, pp. 222–227. MIT, Cambridge (1991b)

    Google Scholar 

  93. Schmidhuber, J.: Exploring the predictable. In: Ghosh, S., Tsutsui, T. (eds.) Advances in Evolutionary Computing, pp. 579–612. Springer, Berlin (2002)

    Google Scholar 

  94. Schmidhuber, J.: Maximizing fun by creating data with easily reducible subjective complexity. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)

    Google Scholar 

  95. Schultz, W.: Predictive reward signal of dopamine neurons. J. Neurophysiol. 80(1), 1–27 (1998)

    Google Scholar 

  96. Schultz, W., Dayan, P., Montague, P.: A neural substrate of prediction and reward. Science 275, 1593–1599 (1997)

    Google Scholar 

  97. Schultz, W., Dickinson, A.: Neuronal coding of prediction errors. Annu. Rev. Neurosci. 23, 473–500 (2000)

    Google Scholar 

  98. Simon, H.A.: The Sciences of the Artificial, 3rd edn. MIT, Cambridge (1996)

    Google Scholar 

  99. Singh, S.P.: Transfer of learning by composing solutions of elemental sequential tasks. Mach. Learn. 8, 323–339 (1992)

    MATH  Google Scholar 

  100. Sirois, S., Mareschal, D.: An interacting systems model of infant habituation. J. Cogn. Neurosci. 16(8), 1352–1362 (2004)

    Google Scholar 

  101. Storck, J., Hochreiter, S., Schmidhuber, J.: Reinforcement-driven information acquisition in non-deterministic environments. In: Proceedings of ICANN’95, vol. 2, pp. 159–164, Paris (1995)

    Google Scholar 

  102. Stout, A., Barto, A.G.: Competence progress intrinsic motivation. In: Proceedings of the 9th International Conference on Development and Learning (ICDL 2010), pp. 257–262. Ann Arbor, USA (2010)

    Google Scholar 

  103. Stout, A., Konidaris, G.D., Barto, A.G.: Intrinsically motivated reinforcement learning: A promising framework for developmental robot learning. In: Proceedings of the AAAI Spring Symposium on Developmental Robotics, Stanford (2005)

    Google Scholar 

  104. Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT, Cambridge (1998)

    MATH  Google Scholar 

  105. Sutton, R., Precup, D., Singh, S.: Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning. Artif. Intell. 112, 181–211 (1999)

    MathSciNet  MATH  Google Scholar 

  106. Sutton, R.S.: Learning to predict by the methods of temporal differences. Mach. Learn. 3, 9–44 (1988)

    Google Scholar 

  107. Taber, M., Das, S., Fibiger, H.: Cortical regulation of subcortical dopamine release: Mediation via the ventral tegmental area. J. Neurochem. 65(3), 1407–1410 (1995)

    Google Scholar 

  108. Tani, J., Nishimoto, R., Paine, R.: Achieving ’organic compositionality’ through self-organization: Reviews on brain-inspired robotics experiments. Neural Netw. 21, 584–603 (2008)

    Google Scholar 

  109. Tani, J., Nolfi, S.: Learning to perceive the world as articulated: An approach for hierarchical learning in sensory-motor systems. Neural Netw. 12, 1131–1141 (1999)

    Google Scholar 

  110. Vigorito, C., Barto, A.: Intrinsically motivated hierarchical skill learning in structured environments. IEEE Trans. Auton. Mental Dev. 2(2), 83–90 (2010)

    Google Scholar 

  111. Weng, J., McClelland, J., Pentland, A., Sporns, O., Stockman, I., Sur, M., Thelen, E.: Autonomous mental development by robots and animals. Science 291, 599–600 (2001)

    Google Scholar 

  112. White, R.W.: Motivation reconsidered: The concept of competence. Psychol. Rev. 66, 297–333 (1959)

    Google Scholar 

  113. Wiering, M., Schmidhuber, J.: Hq-learning. Adap. Behav. 6, 219–246 (1997)

    Google Scholar 

  114. Yamashita, Y., Tani, J.: Emergence of functional hierarchy in a multiple timescale neural network model: A humanoid robot experiment. PLoS Comput. Biol. 4(11), e1000220 (2008)

    Google Scholar 

  115. Yin, H.H., Knowlton, B.J.: The role of the basal ganglia in habit formation. Nat. Rev. Neurosci. 7, 464–476 (2006)

    Google Scholar 

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Acknowledgements

Thanks to Pierre-Yves Oudeyer, Andrew Barto, Kevin Gurney, and Jochen Triesh for their useful comments that substantially helped to improve the paper. Any remaining omission or mistake is our own blame. This research has received funds from the European Commission 7th Framework Programme (FP7/2007-2013), “Challenge 2: Cognitive Systems, Interaction, Robotics,” Grant Agreement No. ICT-IP-231722, Project “IM-CLeVeR: Intrinsically Motivated Cumulative Learning Versatile Robots.”

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Mirolli, M., Baldassarre, G. (2013). Functions and Mechanisms of Intrinsic Motivations. In: Baldassarre, G., Mirolli, M. (eds) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32375-1_3

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