Unlocking the Power of Representations in Long-term Novelty-based Exploration

We introduce Robust Exploration via Clusteringbased Online Density Estimation (RECODE), a nonparametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space. By adapting classical clustering to the nonstationary setting of Deep RL, RECODE can efficiently track state visitation counts over thousands of episodes. We further propose a novel generalization of the inverse dynamics loss, which leverages masked transformer architectures for multi-step prediction; which in conjunction with RECODE achieves a new state-of-the-art in Figure 1: A key result of RECODE is that it allows us to a suite of challenging 3D-exploration tasks in leverage more powerful state representations for long-term DM-HARD-8. RECODE also sets new state-of-theart novelty estimation; enabling to achieve a new state-of-theart in hard exploration Atari games, and is the first in the challenging 3D task suite DM-HARD-8.