Autonomous Agents and Multi-Agent Systems

, Volume 30, Issue 1, pp 30–59 | Cite as

Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning

  • Robert Loftin
  • Bei Peng
  • James MacGlashan
  • Michael L. Littman
  • Matthew E. Taylor
  • Jeff Huang
  • David L. Roberts


For real-world applications, virtual agents must be able to learn new behaviors from non-technical users. Positive and negative feedback are an intuitive way to train new behaviors, and existing work has presented algorithms for learning from such feedback. That work, however, treats feedback as numeric reward to be maximized, and assumes that all trainers provide feedback in the same way. In this work, we show that users can provide feedback in many different ways, which we describe as “training strategies.” Specifically, users may not always give explicit feedback in response to an action, and may be more likely to provide explicit reward than explicit punishment, or vice versa, such that the lack of feedback itself conveys information about the behavior. We present a probabilistic model of trainer feedback that describes how a trainer chooses to provide explicit reward and/or explicit punishment and, based on this model, develop two novel learning algorithms (SABL and I-SABL) which take trainer strategy into account, and can therefore learn from cases where no feedback is provided. Through online user studies we demonstrate that these algorithms can learn with less feedback than algorithms based on a numerical interpretation of feedback. Furthermore, we conduct an empirical analysis of the training strategies employed by users, and of factors that can affect their choice of strategy.


Learning from feedback Reinforcement learning Bayesian inference Interactive learning Machine learning Human–computer interaction 



This work was supported in part by Grants IIS-1149917 and IIS-1319412 from the National Science Foundation.


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Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Robert Loftin
    • 1
  • Bei Peng
    • 2
  • James MacGlashan
    • 3
  • Michael L. Littman
    • 3
  • Matthew E. Taylor
    • 2
  • Jeff Huang
    • 3
  • David L. Roberts
    • 1
  1. 1.Department of Computer ScienceNorth Carolina State UniversityRaleighUSA
  2. 2.School of Electrical Engineering and Computer ScienceWashington State UniversityPullmanUSA
  3. 3.Department of Computer ScienceBrown UniversityProvidenceUSA

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