“Recurrent Environment Simulators”, Silvia Chiappa, Sébastien Racaniere, Daan Wierstra, Shakir Mohamed2017-04-07 (RNN, RL exploration, model-based RL; similar)⁠:

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 computational 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.

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