Recurrent Environment Simulators

Silvia Chiappa, S├ębastien Racaniere, Daan Wierstra, Shakir Mohamed

Nov 04, 2016 (modified: Apr 05, 2017) ICLR 2017 conference submission readers: everyone
  • Abstract: Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous environment simulators from high-dimensional pixel observations by introducing recurrent neural networks that are able to make temporally and spatially coherent predictions for hundreds of time-steps into the future. We present an in-depth analysis of the factors affecting performance, providing the most extensive attempt to advance the understanding of the properties of these models. We address the issue of computationally inefficiency with a model that does not need to generate a high-dimensional image at each time-step. We show that our approach can be used to improve exploration and is adaptable to many diverse environments, namely 10 Atari games, a 3D car racing environment, and complex 3D mazes.
  • Conflicts: google.com
  • Keywords: Deep learning, Unsupervised Learning, Applications

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