Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm

arXiv:2403.16829v1 Announce Type: new
Abstract: Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm to solve entropy-regularized IRL problem. In particular, we employ a stochastic gradient descent update for the reward and a stochastic soft policy iteration update for the policy. Assuming access to a generative model, we prove that our algorithm is guaranteed to recover a reward for which the expert …

abstract algorithm arxiv convergence cs.ai cs.lg dataset entropy expert free gradient reinforcement reinforcement learning solve stochastic type update work