  In this work, we propose algorithms and methods that enable learning
dexterous object manipulation using simulated one- or two-armed robots equipped
with multi-fingered hand end-effectors. Using a parallel GPU-accelerated
physics simulator (Isaac Gym), we implement challenging tasks for these robots,
including regrasping, grasp-and-throw, and object reorientation. To solve these
problems we introduce a decentralized Population-Based Training (PBT) algorithm
that allows us to massively amplify the exploration capabilities of deep
reinforcement learning. We find that this method significantly outperforms
regular end-to-end learning and is able to discover robust control policies in
challenging tasks. Video demonstrations of learned behaviors and the code can
be found at https://sites.google.com/view/dexpbt
