Learning something from nothing: Leveraging implicit human feedback strategies

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Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation (2020, background): “Loftin et al. speed up learning by adapting to different feedback-providing strategies [23, 24]; the Advice framework [11, 6] treats human feedback as direct policy labels and uses a probabilistic model to learn from inconsistent feedback.”
The EMPATHIC Framework for Task Learning from Implicit Human Feedback (2020, background): “Others’ usage of “implicit feedback” has referred to the implied feedback when a human refrains from giving explicit feedback [30, 31], to human biomagnetic signals [32], or to facial expressions [33, 34, 35].”
Agent-Agnostic Human-in-the-Loop Reinforcement Learning (2017, background): “Prior literature has investigated how people can help RL agents learn more efﬁciently through different methods of interaction [32, 24, 28, 36, 42, 48, 21, 17, 25, 43, 23, 49, 46, 44, 30, 10].”
Guiding Reinforcement Learning Exploration Using Natural Language (2017, unknown): “Encoder-decoder networks have been used frequently in other areas, such as machine translation (Luong, Pham, and Manning 2015), to learn how to convert sets of input sequences into desired output sequences.”
Robots That Use Language (2020, background): “A bottle consists of a bottle and a cap, and a person referencing “the bottle” may mean both, or they may say, “Grab the bottle and then turn the cap,”referring”