title: Novelty Search in representational space for sample efficient exploration
abstract: We present a new approach for efficient exploration which leverages a low-dimensional encoding of the environment learned with a combination of model-based and model-free objectives. Our approach uses intrinsic rewards that are based on a weighted distance of nearest neighbors in the low dimensional representational space to gauge novelty.
  We then leverage these intrinsic rewards for sample-efficient exploration with planning routines in representational space.
  One key element of our approach is that we perform more gradient steps in-between every environment step in order to ensure the model accuracy. We test our approach on a number of maze tasks, as well as a control problem and show that our exploration approach is more sample-efficient compared to strong baselines. 
authors: Ruo Yu Tao Vincent François-Lavet Joelle Pineau
hIndex: Joelle Pineau 60