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- W2218252352 abstract "In this demonstration, we allow humans to interactively advise a Mario agent during learning, and observe the resulting changes in performance, as compared to its unadvised counterpart. We do this via a novel potential-based reward shaping framework, capable for the first time of handling the scenario of online feedback." @default.
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- W2218252352 date "2015-05-04" @default.
- W2218252352 modified "2023-09-23" @default.
- W2218252352 title "Shaping Mario with Human Advice" @default.
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- W2218252352 doi "https://doi.org/10.5555/2772879.2773501" @default.
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