References & Citations
Computer Science > Machine Learning
Title: Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm
(Submitted on 25 Mar 2024 (v1), last revised 23 Apr 2024 (this version, v2))
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 is $\varepsilon$-optimal using $\mathcal{O}(1/\varepsilon^{2})$ samples of the Markov decision process (MDP). Furthermore, with $\mathcal{O}(1/\varepsilon^{4})$ samples we prove that the optimal policy corresponding to the recovered reward is $\varepsilon$-close to the expert policy in total variation distance.
Submission history
From: Tingting Ni [view email][v1] Mon, 25 Mar 2024 14:54:42 GMT (25kb)
[v2] Tue, 23 Apr 2024 13:54:27 GMT (25kb)
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