  Recent exploration methods have proven to be a recipe for improving
sample-efficiency in deep reinforcement learning (RL). However, efficient
exploration in high-dimensional observation spaces still remains a challenge.
This paper presents Random Encoders for Efficient Exploration (RE3), an
exploration method that utilizes state entropy as an intrinsic reward. In order
to estimate state entropy in environments with high-dimensional observations,
we utilize a k-nearest neighbor entropy estimator in the low-dimensional
representation space of a convolutional encoder. In particular, we find that
the state entropy can be estimated in a stable and compute-efficient manner by
utilizing a randomly initialized encoder, which is fixed throughout training.
Our experiments show that RE3 significantly improves the sample-efficiency of
both model-free and model-based RL methods on locomotion and navigation tasks
from DeepMind Control Suite and MiniGrid benchmarks. We also show that RE3
allows learning diverse behaviors without extrinsic rewards, effectively
improving sample-efficiency in downstream tasks. Source code and videos are
available at https://sites.google.com/view/re3-rl.
