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Found 4 papers, 2 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.
Large Scale Radio Frequency Signal Classification
We also introduce TorchSig, a signals processing machine learning toolkit that can be used to generate this dataset.
Task-Guided Inverse Reinforcement Learning Under Partial Information
Nevertheless, the resulting formulation is still nonconvex due to the intrinsic nonconvexity of the so-called forward problem, i. e., computing an optimal policy given a reward function, in POMDPs.
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