Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations

In this paper we adopt a representation-centric perspective on exploration in reinforcement learning, viewing exploration fundamentally as a density estimation problem. We investigate the effectiveness of clustering representations for exploration in 3-D environments, based on the observation that the importance of pixel changes between transitions is less pronounced in 3-D environments compared to 2-D environments, where pixel changes between transitions are typically distinct and significant. We propose a method that performs episodic and global clustering on random representations and on …

3-d cluster clustering cs.ai cs.lg dimensions environments exploration importance observation paper perspective pixel reinforcement reinforcement learning representation transitions