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.
We conduct exploration using intrinsic rewards that are based on a weighted distance of nearest neighbors in representational space. This paper proposes a method for efficient exploration in tabular MDPs as well as a simple control environment, using deterministic encoders to learn a low dimensional representation of the environment dynamics. This paper proposes a method of sample-efficient exploration for RL agent using a combination of model-based and model-free approaches with a novelty metric.