Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm
This provides a theoretical guarantee for model-free IRL, which is incremental as it builds on existing entropy-regularized methods but offers new convergence bounds.
The paper tackles the problem of recovering a reward function from expert demonstrations in inverse reinforcement learning by proposing a model-free algorithm with entropy regularization, proving convergence to an ε-optimal reward using O(1/ε²) samples and to an ε-close policy using O(1/ε⁴) samples.
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.