  Deep reinforcement learning (DRL) has revolutionized learning and actuation
in applications such as game playing and robotic control. The cost of data
collection, i.e., generating transitions from agent-environment interactions,
remains a major challenge for wider DRL adoption in complex real-world
problems. Following a cloud-native paradigm to train DRL agents on a GPU cloud
platform is a promising solution. In this paper, we present a scalable and
elastic library ElegantRL-podracer for cloud-native deep reinforcement
learning, which efficiently supports millions of GPU cores to carry out
massively parallel training at multiple levels. At a high-level,
ElegantRL-podracer employs a tournament-based ensemble scheme to orchestrate
the training process on hundreds or even thousands of GPUs, scheduling the
interactions between a leaderboard and a training pool with hundreds of pods.
At a low-level, each pod simulates agent-environment interactions in parallel
by fully utilizing nearly 7,000 GPU CUDA cores in a single GPU. Our
ElegantRL-podracer library features high scalability, elasticity and
accessibility by following the development principles of containerization,
microservices and MLOps. Using an NVIDIA DGX SuperPOD cloud, we conduct
extensive experiments on various tasks in locomotion and stock trading and show
that ElegantRL-podracer substantially outperforms RLlib. Our codes are
available on GitHub.
