Facial feedback for reinforcement learning: a case study and offline analysis using the TAMER framework

Guangliang Li, Hamdi Dibeklioğlu, Shimon Whiteson, Hayley Hung

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
32 Downloads (Pure)

Abstract

Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give copious feedback early in training but very sparsely thereafter. In this article, we investigate the potential of agent learning from trainers’ facial expressions via interpreting them as evaluative feedback. To do so, we implemented TAMER which is a popular interactive reinforcement learning method in a reinforcement-learning benchmark problem—Infinite Mario, and conducted the first large-scale study of TAMER involving 561 participants. With designed CNN–RNN model, our analysis shows that telling trainers to use facial expressions and competition can improve the accuracies for estimating positive and negative feedback using facial expressions. In addition, our results with a simulation experiment show that learning solely from predicted feedback based on facial expressions is possible and using strong/effective prediction models or a regression method, facial responses would significantly improve the performance of agents. Furthermore, our experiment supports previous studies demonstrating the importance of bi-directional feedback and competitive elements in the training interface.

Original languageEnglish
Article number22
Number of pages29
JournalAutonomous Agents and Multi-Agent Systems
Volume34
Issue number1
DOIs
Publication statusPublished - 2020

Keywords

  • Facial expressions
  • Human agent interaction
  • Interactive reinforcement learning
  • Reinforcement learning

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