Browsing: Yannic Kilcher
Commentary of Abstract We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects.…
Abstract: Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in…
Abstract: Deep reinforcement learning agents have achieved state-of-the-art results by directly maximising cumulative reward. However, environments contain a much wider…
Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The…
Don’t watch this if you already know how to solve a merge conflict 🙂 source
Authors: Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell Abstract: In many real-world scenarios, rewards extrinsic to the agent…
Authors: David Ha, Jürgen Schmidhuber Abstract: We explore building generative neural network models of popular reinforcement learning environments. Our world…
Abstract: In recent years, the interest in unsupervised learning of disentangled representations has significantly increased. The key assumption is that…
We present a stochastic non-autoregressive RNN that does not require teacher-forcing for training. The content is based on our 2018…