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ATAL
2015
Springer
2015
Springer
Shaping Mario with Human Advice
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. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning Keywords potential-based reward shaping; human advice
Real-time Traffic| Added | 16 Apr 2016 |
| Updated | 16 Apr 2016 |
| Type | Journal |
| Year | 2015 |
| Where | ATAL |
| Authors | Anna Harutyunyan, Tim Brys, Peter Vrancx, Ann Nowé |
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