• Corpus ID: 219966551

Langevin Dynamics for Inverse Reinforcement Learning of Stochastic Gradient Algorithms

  title={Langevin Dynamics for Inverse Reinforcement Learning of Stochastic Gradient Algorithms},
  author={Vikram Krishnamurthy and George Yin},
Inverse reinforcement learning (IRL) aims to estimate the reward function of optimizing agents by observing their response (estimates or actions). This paper considers IRL when noisy estimates of the gradient of a reward function generated by multiple stochastic gradient agents are observed. We present a generalized Langevin dynamics algorithm to estimate the reward function $R(\theta)$; specifically, the resulting Langevin algorithm asymptotically generates samples from the distribution… 

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Passive stochastic approximation with constant step size and window width
  • G. Yin, K. Yin
  • Computer Science
    IEEE Trans. Autom. Control.
  • 1996
Recursion algorithms combining stochastic approximation and kernel estimation are studied and developed and it is proven that a weak convergence result holds for an interpolated sequence of the iterates.