A Review on Interactive Reinforcement Learning From Human Social Feedback
نویسندگان
چکیده
منابع مشابه
Interactive Learning from Policy-Dependent Human Feedback
For agents and robots to become more useful, they must be able to quickly learn from non-technical users. This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has made the assumption that people provide feedback for decisions that is dependent on the behavior they are teac...
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Neuroscientific studies of social cognition typically employ paradigms in which perceivers draw single-shot inferences about the internal states of strangers. Real world social inference features much different parameters: people often encounter and learn about particular social targets (e.g., friends) over time and receive feedback about when their inferences are correct or incorrect. Here we ...
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As computational agents are increasingly used beyond research labs, their success will depend on their ability to learn new skills and adapt to their dynamic, complex environments. If human users — without programming skills — can transfer their task knowledge to agents, learning can accelerate dramatically, reducing costly trials. The TAMER framework guides the design of agents whose behavior ...
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Inverse reinforcement learning Predicting future behavior Group dynamics Do users change behavior in response to social interactions? We first take a model-free view. Given that a user makes a contribution to a certain community, how likely is the user to visit that community again as a function of the resulting social interactions? We use data from reddit.com, a social media website with many ...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3006254