Intrinsic Motivation and Reinforcement Learning



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.


Intrinsic Motivation Reinforcement Learning Artificial Agent Internal Environment Reward Function 
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.



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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Authors and Affiliations

  1. 1.University of MassachussettsAmherstUSA
  2. 2.Institute of Cognitive Sciences and Technologies, CNRRomaItaly

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