RDIRL is an online deep inverse reinforcement learning method that updates a learned cost after each expert demonstration with a Kalman-style second-order Newton step, and it outperforms batch IRL baselines in simulated control and radar tasks.
Online inverse reinforcement learning for systems with disturbances
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Recursive Deep Inverse Reinforcement Learning
RDIRL is an online deep inverse reinforcement learning method that updates a learned cost after each expert demonstration with a Kalman-style second-order Newton step, and it outperforms batch IRL baselines in simulated control and radar tasks.