  Solving real-world complex tasks using reinforcement learning (RL) without
high-fidelity simulation environments or large amounts of offline data can be
quite challenging. Online RL agents trained in imperfect simulation
environments can suffer from severe sim-to-real issues. Offline RL approaches
although bypass the need for simulators, often pose demanding requirements on
the size and quality of the offline datasets. The recently emerged hybrid
offline-and-online RL provides an attractive framework that enables joint use
of limited offline data and imperfect simulator for transferable policy
learning. In this paper, we develop a new algorithm, called H2O+, which offers
great flexibility to bridge various choices of offline and online learning
methods, while also accounting for dynamics gaps between the real and
simulation environment. Through extensive simulation and real-world robotics
experiments, we demonstrate superior performance and flexibility over advanced
cross-domain online and offline RL algorithms.
