Inverse reinforcement learning (IRL) usually assumes the model of the reward function is pre-specified and estimates the parameter only. However, how to determine a proper reward model is nontrivial. A simplistic model is less likely to contain the real reward function, while a model with high complexity leads to substantial computation cost and risks overfitting. This paper addresses this trade-off in IRL model selection by introducing the structural risk minimization (SRM) method from statistical learning. SRM selects an optimal reward function class from a hypothesis set minimizing both estimation error and model complexity. To formulate an SRM scheme for IRL, we estimate policy gradient by demonstration serving as empirical risk and establish the upper bound of Rademacher complexity of hypothesis classes as model penalty. The learning guarantee is further presented. In particular, we provide explicit SRM for the common linear weighted sum setting in IRL. Simulations demonstrate the performance and efficiency of our scheme.

通过引入结构风险最小化方法，本文解决了逆强化学习模型选择中的权衡问题，以估计误差和模型复杂度为目标，选择最佳的奖励函数类别。具体实施的结构风险最小化包括估计策略梯度和建立模型惩罚的Rademacher复杂度的上界。通过模拟实验验证了该方案的性能和效率。

基于结构风险最小化的未知奖励模型的逆强化学习