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Intrinsically Motivated Learning in Natural and Artificial Systems pp 17–47Cite as

Intrinsic Motivation and Reinforcement Learning

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Abstract

Psychologists distinguish between extrinsically motivated behavior, which is behavior undertaken to achieve some externally supplied reward, such as a prize, a high grade, or a high-paying job, and intrinsically motivated behavior, which is behavior done for its own sake. Is an analogous distinction meaningful for machine learning systems? Can we say of a machine learning system that it is motivated to learn, and if so, is it possible to provide it with an analog of intrinsic motivation? Despite the fact that a formal distinction between extrinsic and intrinsic motivation is elusive, this chapter argues that the answer to both questions is assuredly “yes” and that the machine learning framework of reinforcement learning is particularly appropriate for bringing learning together with what in animals one would call motivation. Despite the common perception that a reinforcement learning agent’s reward has to be extrinsic because the agent has a distinct input channel for reward signals, reinforcement learning provides a natural framework for incorporating principles of intrinsic motivation.

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Notes

  1. 1.

    The phrase computational RL is used here because this framework is not a theory of biological RL despite what it borrows from, and suggests about, biological RL. Throughout this chapter, RL refers to computational RL.

  2. 2.

    RL certainly does not exclude analogs of innate behavioral patterns in artificial agents. The success of many systems using RL methods depends on the careful definition of innate behaviors, as in Hart and Grupen (2012).

  3. 3.

    The term critic is used, and not “teacher”, because in machine learning a teacher provides more informative instructional information, such as directly telling the agent what its actions should have been instead of merely scoring them.

  4. 4.

    It is important to note that the adaptive critic of these methods is inside the RL agent, while the different critic shown in Fig. 1—that provides the primary reward signal—is in the RL agent’s environment.

  5. 5.

    Deci and Ryan (1985) mention that the term intrinsic motivation was first used by Harlow (1950) in a study showing that rhesus monkeys will spontaneously manipulate objects and work for hours to solve complicated mechanical puzzles without any explicit rewards.

  6. 6.

    Schmidhuber (2009) would argue that it is the other way around—that control is a result of behavior directed to improve predictive models, which in this author’s opinion is at odds with what we know about evolution.

  7. 7.

    These comments apply to the “passive” form of supervised learning and not necessarily to the extension known as “active learning” (Settles 2009), in which the learning agent itself chooses training examples. Although beyond this chapter’s scope, active supervised learning is indeed relevant to the subject of intrinsic motivation.

  8. 8.

    We are relying on a commonsense notion of an organism’s boundary with its external environment, recognizing that this may be not be easy to define.

  9. 9.

    Figure 2 shows the organism containing a single RL agent, but an organism might contain many, each possibly having its own reward signal. Although not considered here, the multi-agent RL case (Busoniu et al. 2008) poses many challenges and opportunities.

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Acknowledgements

The author thanks Satinder Singh, Rich Lewis, and Jonathan Sorg for developing the evolutionary perspective on this subject and for their important insights, and colleagues Sridhar Mahadevan and Rod Grupen, along with current and former members of the Autonomous Learning Laboratory who have participated in discussing intrinsically motivated reinforcement learning: Bruno Castro da Silva, Will Dabney, Jody Fanto, George Konidaris, Scott Kuindersma, Scott Niekum, Özgür Şimşek, Andrew Stout, Phil Thomas, Chris Vigorito, and Pippin Wolfe. The author thanks Pierre-Yves Oudeyer for his many helpful suggestions, especially regarding non-RL approaches to intrinsic motivation. Special thanks are due to Prof. John W. Moore, whose expert guidance through the psychology literature was essential to writing this chapter, though any misrepresentation of psychological thought is strictly due to the author. This research has benefitted immensely from the author’s association with the European Community 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.” Some of the research described here was supported by the National Science Foundation under Grant No. IIS-0733581 and by the Air Force Office of Scientific Research under grant FA9550-08-1-0418. Any opinions, findings, conclusions, or recommendations expressed here are those of the author and do not necessarily reflect the views of the sponsors.

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    Andrew G. Barto

  2. Institute of Cognitive Sciences and Technologies, CNR, Roma, Italy

    Andrew G. Barto

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Barto, A.G. (2013). Intrinsic Motivation and Reinforcement Learning. 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_2

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