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Solving real-world complex tasks using reinforcement learning (RL) withouthigh-fidelity simulation environments or large amounts of offline data can bequite challenging. Online RL agents trained in imperfect simulationenvironments can suffer from severe sim-to-real issues. Offline RL approachesalthough bypass the need for simulators, often pose demanding requirements onthe size and quality of the offline datasets. The recently emerged hybridoffline-and-online RL provides an attractive framework that enables joint useof limited offline data and imperfect simulator for transferable policylearning. In this paper, we develop a new algorithm, called H2O+, which offersgreat flexibility to bridge various choices of offline and online learningmethods, while also accounting for dynamics gaps between the real andsimulation environment. Through extensive simulation and real-world roboticsexperiments, we demonstrate superior performance and flexibility over advancedcross-domain online and offline RL algorithms.

H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps