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Nicholas Waytowich

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AAAI Conference 2024 Conference Paper

Rating-Based Reinforcement Learning

  • Devin White
  • Mingkang Wu
  • Ellen Novoseller
  • Vernon J. Lawhern
  • Nicholas Waytowich
  • Yongcan Cao

This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning. Different from the existing preference-based and ranking-based reinforcement learning paradigms, based on human relative preferences over sample pairs, the proposed rating-based reinforcement learning approach is based on human evaluation of individual trajectories without relative comparisons between sample pairs. The rating-based reinforcement learning approach builds on a new prediction model for human ratings and a novel multi-class loss function. We conduct several experimental studies based on synthetic ratings and real human ratings to evaluate the effectiveness and benefits of the new rating-based reinforcement learning approach.

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AAAI Conference 2018 Conference Paper

Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces

  • Garrett Warnell
  • Nicholas Waytowich
  • Vernon Lawhern
  • Peter Stone

While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data. One way to increase the speed at which agents are able to learn to perform tasks is by leveraging the input of human trainers. Although such input can take many forms, real-time, scalar-valued feedback is especially useful in situations where it proves difficult or impossible for humans to provide expert demonstrations. Previous approaches have shown the usefulness of human input provided in this fashion (e. g. , the TAMER framework), but they have thus far not considered high-dimensional state spaces or employed the use of deep learning. In this paper, we do both: we propose Deep TAMER, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer. We demonstrate Deep TAMER’s success by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of BOWLING - a task that has proven difficult for even state-of-the-art reinforcement learning methods.

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