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We present a new approach for efficient exploration which leverages alow-dimensional encoding of the environment learned with a combination ofmodel-based and model-free objectives. Our approach uses intrinsic rewards thatare based on the distance of nearest neighbors in the low dimensionalrepresentational space to gauge novelty. We then leverage these intrinsicrewards for sample-efficient exploration with planning routines inrepresentational space for hard exploration tasks with sparse rewards. One keyelement of our approach is the use of information theoretic principles to shapeour representations in a way so that our novelty reward goes beyond pixelsimilarity. We test our approach on a number of maze tasks, as well as acontrol problem and show that our exploration approach is more sample-efficientcompared to strong baselines.

Novelty Search in Representational Space for Sample Efficient Exploration