Learning from expert behavior or demonstrations, including behavior cloning, covariate shift, inverse reward inference, and combining demonstrations with environment feedback.
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8 articles in this tag.
A behavior-cloned agent can look flawless on training data and fall apart the moment it makes one small mistake. That is not bad luck. It is the signature…
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A cloned policy can score 100% on the states it trained on and still walk off the path the moment it drives itself. That gap is the whole experiment.
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A cloned policy can score 98% on held-out demonstration states and still walk off a cliff in four seconds. The metric and the rollout are measuring two…
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Pure reinforcement learning wastes samples rediscovering what a demonstrator already solved. Pure imitation freezes at the demonstrator's ceiling. The real…
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A policy cloned from demonstrations can look brilliant and still be useless. It scores well on the states it was shown, then stalls the moment the world…
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You have a stack of expert demonstrations. Maybe you recorded a human driving a car, or you logged the decisions of a system that already works well. The…
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Demonstrations tell you what an expert did. Inverse reinforcement learning asks why—and the answer is never uniquely determined by the behavior itself.
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Two reward functions. One demonstrated path. No way to tell them apart from behavior alone.
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