GEAR: A GPU-Centric Experience Replay System for Large Reinforcement
  Learning Models
Hanjing Wang Man-Kit Sit Congjie He Ying Wen Weinan Zhang Jun Wang Yaodong Yang Luo Mai
This paper introduces a distributed, GPU-centric experience replay system,
GEAR, designed to perform scalable reinforcement learning (RL) with large
sequence models (such as transformers). With such models, existing systems such
as Reverb face considerable bottlenecks in memory, computation, and
communication. GEAR, however, optimizes memory efficiency by enabling the
memory resources on GPU servers (including host memory and device memory) to
manage trajectory data. Furthermore, it facilitates decentralized GPU devices
to expedite various trajectory selection strategies, circumventing
computational bottlenecks. GEAR is equipped with GPU kernels capable of
collecting trajectories using zero-copy access to host memory, along with
remote-directed-memory access over InfiniBand, improving communication
efficiency. Cluster experiments have shown that GEAR can achieve performance
levels up to 6x greater than Reverb when training state-of-the-art large RL
models. GEAR is open-sourced at https://github.com/bigrl-team/gear.