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- W2951500848 abstract "We study the model of projective simulation (PS) which is a novel approach to artificial intelligence (AI). Recently it was shown that the PS agent performs well in a number of simple task environments, also when compared to standard models of reinforcement learning (RL). In this paper we study the performance of the PS agent further in more complicated scenarios. To that end we chose two well-studied benchmarking problems, namely the grid-world and the mountain-car problem, which challenge the model with large and continuous input space. We compare the performance of the PS agent model with those of existing models and show that the PS agent exhibits competitive performance also in such scenarios." @default.
- W2951500848 created "2019-06-27" @default.
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- W2951500848 date "2014-05-21" @default.
- W2951500848 modified "2023-09-27" @default.
- W2951500848 title "Projective simulation applied to the grid-world and the mountain-car problem" @default.
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