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Found 2 papers, 1 papers with code
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning
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.
Task-Guided IRL in POMDPs that Scales
First, they require an excessive amount of data due to the information asymmetry between the expert and the learner.
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