On the Effective Horizon of Inverse Reinforcement Learning

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; they 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 e"ciency of IRL algorithms. Interestingly, an e!ective 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 over!tting with limited data. This analysis provides a guide for the principled choice of the e#ective horizon for IRL. It also prompts us to reexamine the classic IRL formulation: it is more natural to learn jointly the reward and the e#ective horizon rather than the reward alone with a given horizon. To validate our! ndings, we implement a cross-validation extension and the experimental results support the theoretical analysis. The project page and code are publicly available.