Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-24T12:36:36.568Z Has data issue: false hasContentIssue false

Chapter 3 - Curiosity-Driven Exploration

Diversity of Mechanisms and Functions

from Part I - What Drives Humans to Seek Information?

Published online by Cambridge University Press:  19 May 2022

By
Alexandr Ten ,
Pierre-Yves Oudeyer  and
Clément Moulin-Frier
Edited by
Irene Cogliati Dezza ,
Eric Schulz  and
Charley M. Wu
Irene Cogliati Dezza
Affiliation:
University College London
Eric Schulz
Affiliation:
Max-Planck-Institut für biologische Kybernetik, Tübingen
Charley M. Wu
Affiliation:
Eberhard-Karls-Universität Tübingen, Germany
Get access

Summary

Intrinsically motivated information-seeking, also called curiosity-driven exploration, is widely believed to be a key ingredient for autonomous learning in the real world. Such forms of spontaneous exploration have been studied in multiple independent lines of computational research, producing a diverse range of algorithmic models that capture different aspects of these processes. These algorithms resolve some of the limitations of neurocognitive theories by formally describing computational functions and algorithmic implementations of intrinsically motivated learning. Moreover, they reveal a high diversity of effective forms of intrinsically motivated information-seeking that can be characterized along different mechanistic and functional dimensions. This chapter aims at reviewing different classes of algorithms and highlighting several important dimensions of variation among them. Identifying these dimensions provides means for structuring a comprehensive taxonomy of approaches. We believe this exercise to be useful in working toward a general computational account of information-seeking. Such an account should facilitate the proposition of new hypotheses about information-seeking in humans and complement the existing psychological theory of curiosity.

Keywords

Curiosityexplorationintrinsic motivationalgorithmsAI
Type
Chapter
Information
The Drive for Knowledge
The Science of Human Information Seeking
 , pp. 53 - 76
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andreae, P. M., & Andreae, J. H. (1978). A teachable machine in the real world. International Journal of Man-Machine Studies, 10(3), 301–312.Google Scholar
Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., … & Zaremba, W. (2018). Hindsight experience replay. arXiv preprint arXiv:1707.01495.Google Scholar
Aubret, A., Matignon, L., & Hassas, S. (2019). A survey on intrinsic motivation in reinforcement learning. arXiv preprint arXiv:1908.06976.Google Scholar
Baker, B., Kanitscheider, I., Markov, T., Wu, Y., Powell, G., McGrew, B., & Mordatch, I. (2020). Emergent tool use from multi-agent autocurricula. arXiv preprint arXiv:1909.07528.Google Scholar
Baranes, A., & Oudeyer, P. Y. (2009). R-iac: Robust intrinsically motivated exploration and active learning. IEEE Transactions on Autonomous Mental Development, 1(3), 155–169.Google Scholar
Baranes, A., & Oudeyer, P. Y. (2013). Active learning of inverse models with intrinsically motivated goal exploration in robots. Robotics and Autonomous Systems, 61(1), 49–73.CrossRefGoogle Scholar
Barron, A. B., Hebets, E. A., Cleland, T. A., Fitzpatrick, C. L., Hauber, M. E., & Stevens, J. R. (2015). Embracing multiple definitions of learning. Trends in Neurosciences, 38(7), 405–407.Google Scholar
Bazhydai, M., Twomey, K., & Westermann, G. (2021). Curiosity and Exploration. In Benson, J. B. (Ed.), Encyclopedia of Infant and Early Childhood Development (2nd ed.). Elsevier, pp. 370–378.Google Scholar
Bellemare, M., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D., & Munos, R. (2016). Unifying count-based exploration and intrinsic motivation. Advances in Neural Information Processing Systems, 29, 1471–1479.Google Scholar
Benureau, F. C., & Oudeyer, P. Y. (2016). Behavioral diversity generation in autonomous exploration through reuse of past experience. Frontiers in Robotics and AI, 3, 8.Google Scholar
Berseth, G., Geng, D., Devin, C., Rhinehart, N., Finn, C., Jayaraman, D., & Levine, S. (2021). SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments. arXiv preprint arXiv:1912.05510.Google Scholar
Bougie, N., & Ichise, R. (2020). Skill-based curiosity for intrinsically motivated reinforcement learning. Machine Learning, 109(3), 493–512.Google Scholar
Bougie, N., & Ichise, R. (2021). Fast and slow curiosity for high-level exploration in reinforcement learning. Applied Intelligence, 51(2), 1086–1107.Google Scholar
Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., & Efros, A. A. (2018). Large-scale study of curiosity-driven learning. arXiv preprint arXiv:1808.04355.Google Scholar
Caligiore, D., Ferrauto, T., Parisi, D., Accornero, N., Capozza, M., & Baldassarre, G. (2008). Using motor babbling and hebb rules for modeling the development of reaching with obstacles and grasping. In International Conference on Cognitive Systems (Vol. 13, pp. 22–23). www.researchgate.net/publication/227945187_Using_Motor_Babbling_and_Hebb_Rules_for_Modeling_the_Development_of_Reaching_with_Obstacles_and_Grasping.Google Scholar
Chu, J., & Schulz, L. E. (2020). Play, curiosity, and cognition. Annual Review of Developmental Psychology, 2, 317–343.Google Scholar
Clement, B., Oudeyer, P. Y., & Lopes, M. (2016). A Comparison of Automatic Teaching Strategies for Heterogeneous Student Populations. Proceedings of the 9th International Conference on Educational Data Mining, Raleigh, USA.Google Scholar
Clement, B., Roy, D., Oudeyer, P. Y., & Lopes, M. (2015). Multi-armed bandits for intelligent tutoring systems. arXiv preprint arXiv:1310.3174.Google Scholar
Cohn, D. A., Ghahramani, Z., & Jordan, M. I. (1996). Active learning with statistical models. Journal of Artificial Intelligence Research, 4, 129–145.Google Scholar
Colas, C., Fournier, P., Chetouani, M., Sigaud, O., & Oudeyer, P. Y. (2019, May). CURIOUS: intrinsically motivated modular multi-goal reinforcement learning. In International conference on machine learning (pp. 1331–1340). PMLR. http://proceedings.mlr.press/v97/colas19a.html.Google Scholar
Colas, C., Karch, T., Lair, N., Dussoux, J. M., Moulin-Frier, C., Dominey, P. F., & Oudeyer, P. Y. (2020). Language as a cognitive tool to imagine goals in curiosity-driven exploration. arXiv preprint arXiv:2002.09253.Google Scholar
Colas, C., Karch, T., Sigaud, O., & Oudeyer, P. Y. (2021). Intrinsically motivated goal-conditioned reinforcement learning: a short survey. arXiv preprint arXiv:2012.09830.Google Scholar
Colas, C., Sigaud, O., & Oudeyer, P. Y. (2018). Gep-pg: Decoupling exploration and exploitation in deep reinforcement learning algorithms. In International conference on machine learning (pp. 1039–1048). PMLR. http://proceedings.mlr.press/v80/colas18a.html.Google Scholar
Delmas, A., Clement, B., Oudeyer, P. Y., & Sauzéon, H. (2018). Fostering health education with a serious game in children with asthma: pilot studies for assessing learning efficacy and automatized learning personalization. In Frontiers in Education (Vol. 3, p. 99). Frontiers. https://doi.org/10.3389/feduc.2018.00099.Google Scholar
Dobzhansky, T. (1973). Nothing in biology makes sense except in the light of evolution. The American Biology Teacher, 75(2), 87–91.Google Scholar
Etcheverry, M., Moulin-Frier, C., & Oudeyer, P. Y. (2021). Hierarchically organized latent modules for exploratory search in morphogenetic systems. arXiv preprint arXiv:2007.01195.Google Scholar
Florensa, C., Held, D., Geng, X., & Abbeel, P. (2018, July). Automatic goal generation for reinforcement learning agents. In International conference on machine learning (pp. 1515–1528). PMLR. http://proceedings.mlr.press/v80/florensa18a.html.Google Scholar
Fogarty, L., & Creanza, N. (2017). The niche construction of cultural complexity: interactions between innovations, population size and the environment. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1735), 20160428.Google Scholar
Forestier, S., Portelas, R., Mollard, Y., & Oudeyer, P. Y. (2017). Intrinsically motivated goal exploration processes with automatic curriculum learning. arXiv preprint arXiv:1708.02190.Google Scholar
Gershman, S. J. (2019). Uncertainty and exploration. Decision, 6(3), 277.CrossRefGoogle ScholarPubMed
Gopnik, A. (2020). Childhood as a solution to explore–exploit tensions. Philosophical Transactions of the Royal Society B, 375(1803), 20190502.Google Scholar
Gottlieb, J., & Oudeyer, P. Y. (2018). Towards a neuroscience of active sampling and curiosity. Nature Reviews Neuroscience, 19(12), 758–770.Google Scholar
Gottlieb, J., Oudeyer, P. Y., Lopes, M., & Baranes, A. (2013). Information-seeking, curiosity, and attention: computational and neural mechanisms. Trends in Cognitive Sciences, 17(11), 585–593.Google Scholar
Grizou, J., Points, L. J., Sharma, A., & Cronin, L. (2020). A curious formulation robot enables the discovery of a novel protocell behavior. Science Advances, 6(5), eaay4237.Google Scholar
Gross, M. E., Zedelius, C. M., & Schooler, J. W. (2020). Cultivating an understanding of curiosity as a seed for creativity. Current Opinion in Behavioral Sciences, 35, 77–82.CrossRefGoogle Scholar
Haber, N., Mrowca, D., Fei-Fei, L., & Yamins, D. L. (2018). Learning to play with intrinsically-motivated self-aware agents. arXiv preprint arXiv:1802.07442.Google Scholar
Harlow, H. F., Harlow, M. K., & Meyer, D. R. (1950). Learning motivated by a manipulation drive. Journal of Experimental Psychology, 40(2), 228.Google Scholar
Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127.Google Scholar
Holm, L., Wadenholt, G., & Schrater, P. (2019). Episodic curiosity for avoiding asteroids: Per-trial information gain for choice outcomes drive information seeking. Scientific Reports, 9(1), 1–16.Google Scholar
Hull, C. L. (1943). Principles of behavior: An introduction to behavior theory. Appleton-Century.Google Scholar
Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., & Kavukcuoglu, K. (2016). Reinforcement learning with unsupervised auxiliary tasks. arXiv preprint arXiv:1611.05397.Google Scholar
Jaques, N., Lazaridou, A., Hughes, E., Gulcehre, C., Ortega, P., Strouse, D. J., … & De Freitas, N. (2019, May). Social influence as intrinsic motivation for multi-agent deep reinforcement learning. In International Conference on Machine Learning (pp. 3040–3049). PMLR. http://proceedings.mlr.press/v97/jaques19a.html.Google Scholar
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.Google Scholar
Jordan, M. I., & Rumelhart, D. E. (1992). Forward models: Supervised learning with a distal teacher. Cognitive Science, 16(3), 307–354.Google Scholar
Kaplan, F., & Oudeyer, P. Y. (2007). In search of the neural circuits of intrinsic motivation. Frontiers in Neuroscience, 1, 17.CrossRefGoogle ScholarPubMed
Kim, K., Sano, M., De Freitas, J., Haber, N., & Yamins, D. (2020). Active world model learning with progress curiosity. In International conference on machine learning (pp. 5306–5315). PMLR. https://proceedings.mlr.press/v119/kim20e.html.Google Scholar
Laversanne-Finot, A., Péré, A., & Oudeyer, P. Y. (2018). Curiosity driven exploration of learned disentangled goal spaces. In Conference on Robot Learning (pp. 487–504). PMLR. https://proceedings.mlr.press/v87/laversanne-finot18a.html.Google Scholar
Laversanne-Finot, A., Péré, A., & Oudeyer, P. Y. (2021). Intrinsically motivated exploration of learned goal spaces. Frontiers in Neurorobotics, 14, 109.Google Scholar
Lefort, M., & Gepperth, A. (2015). Active learning of local predictable representations with artificial curiosity. In 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 228–233). IEEE. https://ieeexplore.ieee.org/abstract/document/7346145.Google Scholar
Leibo, J. Z., Hughes, E., Lanctot, M., & Graepel, T. (2019). Autocurricula and the emergence of innovation from social interaction: A manifesto for multi-agent intelligence research. arXiv preprint arXiv:1903.00742.Google Scholar
Lin, B., Cecchi, G., Bouneffouf, D., Reinen, J., & Rish, I. (2019). A story of two streams: Reinforcement learning models from human behavior and neuropsychiatry. arXiv preprint arXiv:1906.11286.Google Scholar
Linke, C., Ady, N. M., White, M., Degris, T., & White, A. (2020). Adapting behavior via intrinsic reward: A survey and empirical study. Journal of Artificial Intelligence Research, 69, 1287–1332.Google Scholar
Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75.Google Scholar
Lucas, R. E. (2004). The industrial revolution: Past and future. Economic Education Bulletin, 44(8), 1–8. www.aier.org/wp-content/uploads/2013/11/EEB-8.04-IndustRev.pdf.Google Scholar
Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition: The realization of the living (Vol. 42). Springer Science & Business Media.CrossRefGoogle Scholar
McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11–38.Google Scholar
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 (pp. 49–72). Springer.Google Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., … & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.Google Scholar
Moulin-Frier, C., Nguyen, S. M., & Oudeyer, P. Y. (2014). Self-organization of early vocal development in infants and machines: the role of intrinsic motivation. Frontiers in Psychology, 4, 1006.CrossRefGoogle ScholarPubMed
Moulin-Frier, C., & Oudeyer, P. Y. (2013, August). Exploration strategies in developmental robotics: A unified probabilistic framework. In 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL) (pp. 1–6). IEEE. https://doi.org/10.1109/DevLrn.2013.6652535.Google Scholar
Nair, A., Pong, V., Dalal, M., Bahl, S., Lin, S., & Levine, S. (2018). Visual reinforcement learning with imagined goals. arXiv preprint arXiv:1807.04742.Google Scholar
Nake, F. (1976). Ästhetik als Informationsverarbeitung: Grundlagen und Anwendungen der Informatik im Bereich ästhetischer Produktion und Kritik. Journal of Aesthetics and Art Criticism, 34(3).Google Scholar
Nguyen, S. M., & Oudeyer, P. Y. (2012). Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner. Paladyn, 3(3), 136–146.Google Scholar
Eleni Nisioti, Katia Jodogne-del Litto, Clément Moulin-Frier. Grounding an Ecological Theory of Artificial Intelligence in Human Evolution. NeurIPS 2021 - Conference on Neural Information Processing Systems / Workshop: Ecological Theory of Reinforcement Learning, Dec 2021, virtual event, France. (hal-03446961v2)Google Scholar
Oller, D. K. (2000). The emergence of the speech capacity. Psychology Press.Google Scholar
Oudeyer, P. Y. (2018). Computational theories of curiosity-driven learning. arXiv preprint arXiv:1802.10546.Google Scholar
Oudeyer, P. Y., & Kaplan, F. (2006). Discovering communication. Connection Science, 18(2), 189–206.Google Scholar
Oudeyer, P. Y., & Kaplan, F. (2009). What is intrinsic motivation? A typology of computational approaches. Frontiers in Neurorobotics, 1, 6.Google Scholar
Oudeyer, P. Y., Kaplan, F., & Hafner, V. V. (2007). Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation, 11(2), 265–286.Google Scholar
Oudeyer, P. Y., & Smith, L. B. (2016). How evolution may work through curiosity‐driven developmental process. Topics in Cognitive Science, 8(2), 492–502.Google Scholar
Pan, M., Huang, A., Wang, G., Zhang, T., & Li, X. (2020). Reinforcement learning based curiosity-driven testing of android applications. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 153–164). https://doi.org/10.1145/3395363.3397354.CrossRefGoogle Scholar
Pathak, D., Agrawal, P., Efros, A. A., & Darrell, T. (2017). Curiosity-driven exploration by self-supervised prediction. In International conference on machine learning (pp. 2778–2787). PMLR. http://proceedings.mlr.press/v70/pathak17a.html.Google Scholar
Poli, F., Serino, G., Mars, R. B., & Hunnius, S. (2020). Infants tailor their attention to maximize learning. Science Advances, 6(39), eabb5053.Google Scholar
Pong, V. H., Dalal, M., Lin, S., Nair, A., Bahl, S., & Levine, S. (2020). Skew-fit: State-covering self-supervised reinforcement learning. arXiv preprint arXiv:1903.03698.Google Scholar
Potts, R. (2013). Hominin evolution in settings of strong environmental variability. Quaternary Science Reviews, 73, 1–13.Google Scholar
Reinke, C., Etcheverry, M., & Oudeyer, P. Y. (2020). Intrinsically motivated discovery of diverse patterns in self-organizing systems. arXiv preprint arXiv:1908.06663.Google Scholar
Rolf, M., Steil, J. J., & Gienger, M. (2010). Goal babbling permits direct learning of inverse kinematics. IEEE Transactions on Autonomous Mental Development, 2(3), 216–229.Google Scholar
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68.Google Scholar
Saegusa, R., Metta, G., Sandini, G., & Sakka, S. (2009). Active motor babbling for sensorimotor learning. In 2008 IEEE International Conference on Robotics and Biomimetics (pp. 794–799). IEEE. https://doi.org/10.1109/ROBIO.2009.4913101.Google Scholar
Santucci, V. G., Baldassarre, G., & Mirolli, M. (2013). Which is the best intrinsic motivation signal for learning multiple skills? Frontiers in Neurorobotics, 7, 22.Google Scholar
Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2016). Prioritized experience replay. arXiv preprint arXiv:1511.05952.Google Scholar
Schmidhuber, J. (1991a). A possibility for implementing curiosity and boredom in model-building neural controllers. In Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats (pp. 222–227). https://doi.org/10.7551/mitpress/3115.003.0030.Google Scholar
Schmidhuber, J. (1991b). Curious model-building control systems. In Proc. International Joint Conference on Neural Networks, Singapore City, November 18–21, 1991, (pp. 1458–1463). www.scirp.org/(S(lz5mqp453ed%20snp55rrgjct55))/reference/referencespapers.aspx?referenceid=1385254.Google Scholar
Schueller, W., Loreto, V., & Oudeyer, P. Y. (2018). Complexity reduction in the negotiation of new lexical conventions. arXiv preprint arXiv:1805.05631.Google Scholar
Singh, S., Lewis, R. L., Barto, A. G., & Sorg, J. (2010). Intrinsically motivated reinforcement learning: An evolutionary perspective. IEEE Transactions on Autonomous Mental Development, 2(2), 70–82.CrossRefGoogle Scholar
Stout, A., & Barto, A. G. (2010, August). Competence progress intrinsic motivation. In 2010 IEEE 9th International Conference on Development and Learning (pp. 257–262). IEEE. http://citeseerx.ist.psu.edu/viewdoc/summary;jsessionid=837FA22F4803E257348D38A7397B2774?doi=10.1.1.224.71.Google Scholar
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.Google Scholar
Takahashi, K., Ogata, T., Nakanishi, J., Cheng, G., & Sugano, S. (2017). Dynamic motion learning for multi-DOF flexible-joint robots using active–passive motor babbling through deep learning. Advanced Robotics, 31(18), 1002–1015.Google Scholar
Tang, H., Houthooft, R., Foote, D., Stooke, A., Chen, X., Duan, Y., … & Abbeel, P. (2017). #Exploration: A study of count-based exploration for deep reinforcement learning. Advances in Neural Information Processing Systems. Presented at the 31st Conference on Neural Information Processing Systems (NIPS), 2017. In 31st Conference on Neural Information Processing Systems (NIPS) (Vol. 30, pp. 1–18).Google Scholar
Ten, A., Kaushik, P., Oudeyer, P. Y., & Gottlieb, J. (2021). Humans monitor learning progress in curiosity-driven exploration. Nature Communications, 12(1), 1–10.Google Scholar
Thrun, S. (1995). A lifelong learning perspective for mobile robot control. In Intelligent robots and systems (pp. 201–214). Elsevier Science BV.Google Scholar
Twomey, K. E., & Westermann, G. (2018). Curiosity‐based learning in infants: a neurocomputational approach. Developmental Science, 21(4), e12629.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×