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Found 3 papers, 1 papers with code
Training an Interactive Helper
In this paper, we investigate if, and how, a "helper" agent can be trained to interactively adapt their behavior to maximize the reward of another agent, whom we call the "prime" agent, without observing their reward or receiving explicit demonstrations.
Learning to Interactively Learn and Assist
Most importantly, we find that our approach produces an agent that is capable of learning interactively from a human user, without a set of explicit demonstrations or a reward function, and achieving significantly better performance cooperatively with a human than a human performing the task alone.
Active One-shot Learning
Recent advances in one-shot learning have produced models that can learn from a handful of labeled examples, for passive classification and regression tasks.
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