<ul><li>Inverse reinforcement learning (IRL) focuses on selecting the reward function model using structural risk minimization (SRM).</li><li>IRL tackles the trade-off between a simplistic model and one with high complexity to obtain the ideal reward function.</li><li>The SRM framework selects the optimal reward function class that minimizes both estimation error and model complexity.</li><li>Simulations show the algorithm's performance and efficiency in the linear weighted sum setting.</li></ul>

Model Selection for Inverse Reinforcement Learning via Structural Risk Minimization

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