Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning
AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, 30(1), 30–59.
author keywords: Learning from feedback; Reinforcement learning; Bayesian inference; Interactive learning; Machine learning; Human-computer interaction
TL;DR:
A probabilistic model of trainer feedback is presented that describes how a trainer chooses to provide explicit reward and/or explicit punishment and two novel learning algorithms are developed which take trainer strategy into account, and can therefore learn from cases where no feedback is provided.
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