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Found 3 papers, 1 papers with code
Proximal Point Imitation Learning
Thanks to PPM, we avoid nested policy evaluation and cost updates for online IL appearing in the prior literature.
Stochastic convex optimization for provably efficient apprenticeship learning
We consider large-scale Markov decision processes (MDPs) with an unknown cost function and employ stochastic convex optimization tools to address the problem of imitation learning, which consists of learning a policy from a finite set of expert demonstrations.
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
We consider large-scale Markov decision processes with an unknown cost function and address the problem of learning a policy from a finite set of expert demonstrations.
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