| Hi, thanks for the feedback! Honestly we didn't intend to over-hype the results. We took the terms from existing works that we knew: 1) Alex Graves on Hallucination with Recurrent Neural Networks, a 2015 lecture at the University of Oxford from a course by Nando de Freitas (highly recommended). http://www.creativeai.net/posts/kp4bTG993JTQcqy2d/alex-grave... 2) Generating Sequences With Recurrent Neural Networks https://arxiv.org/abs/1308.0850 "Assuming the predictions are probabilistic, novel sequences can be generated from a trained network by iteratively sampling from the network’s output distribution, then feeding in the sample as input at the next step. In other words by making the network treat its inventions as if they were real, much like a person dreaming." There are other terms, such as Imagination, also used in the literature: 3) Imagination-Augmented Agents for Deep Reinforcement Learning https://arxiv.org/abs/1707.06203 4) Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning http://proceedings.mlr.press/v78/kalweit17a/kalweit17a.pdf In our work, the procedure is closer to the approaches in (1) and (2), rather than the "Imagination" approach in (3) and (4) where there are more subtle differences (i.e. planning), so we followed the terms in (1) and (2). |