Paper ID: 2307.06541

On the Effective Horizon of Inverse Reinforcement Learning

Yiqing Xu, Finale Doshi-Velez, David Hsu

Inverse reinforcement learning (IRL) algorithms often rely on (forward) reinforcement learning or planning over a given time horizon to compute an approximately optimal policy for a hypothesized reward function and then match this policy with expert demonstrations. The time horizon plays a critical role in determining both the accuracy of reward estimates and the computational efficiency of IRL algorithms. Interestingly, an \emph{effective time horizon} shorter than the ground-truth value often produces better results faster. This work formally analyzes this phenomenon and provides an explanation: the time horizon controls the complexity of an induced policy class and mitigates overfitting with limited data. This analysis serves as a guide for the principled choice of the effective horizon for IRL. It also prompts us to re-examine the classic IRL formulation: it is more natural to learn jointly the reward and the effective horizon rather than the reward alone with a given horizon. To validate our findings, we implement a cross-validation extension and the experimental results confirm the theoretical analysis.

Submitted: Jul 13, 2023