Shaping Mario with Human Advice (Demonstration)

TitleShaping Mario with Human Advice (Demonstration)
Publication TypeConference Paper
Year of Publication2015
AuthorsHarutyunyan, A, Brys, T, Vrancx, P, Nowe, A
Conference NameInternational Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-15)
PublisherTo appear
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.