A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning
CoRR(2023)
摘要
We propose and demonstrate a compositional framework for training and
verifying reinforcement learning (RL) systems within a multifidelity
sim-to-real pipeline, in order to deploy reliable and adaptable RL policies on
physical hardware. By decomposing complex robotic tasks into component subtasks
and defining mathematical interfaces between them, the framework allows for the
independent training and testing of the corresponding subtask policies, while
simultaneously providing guarantees on the overall behavior that results from
their composition. By verifying the performance of these subtask policies using
a multifidelity simulation pipeline, the framework not only allows for
efficient RL training, but also for a refinement of the subtasks and their
interfaces in response to challenges arising from discrepancies between
simulation and reality. In an experimental case study we apply the framework to
train and deploy a compositional RL system that successfully pilots a Warthog
unmanned ground robot.
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