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An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model

Created by
  • Haebom

Author

Enoch H. Kang, Hema Yoganarasimhan, Lalit Jain

Outline

This paper studies the offline maximum entropy regularization inverse reinforcement learning (offline MaxEnt-IRL) problem in machine learning, specifically the dynamic discrete choice (DDC) model. The goal is to recover the reward or Q function that governs agent behavior from offline behavioral data. We propose a globally convergent gradient-based method to solve this problem without the restrictive assumption of linearly parameterized rewards. The novelty of this study lies in introducing an empirical risk minimization (ERM)-based IRL/DDC framework that avoids the need for explicit state transition probability estimation in the Bellman equation. Furthermore, the proposed method is compatible with nonparametric estimation techniques such as neural networks. Therefore, the proposed method has the potential to extend to high-dimensional, infinite state spaces. The key theoretical insight of this study is that the Bellman residual satisfies the Polyak-Lojasiewicz (PL) condition, which is weaker than strong convexity but sufficient to guarantee fast global convergence. A series of synthetic experiments demonstrate that the proposed method consistently outperforms benchmark methods and state-of-the-art alternatives.

Takeaways, Limitations

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Takeaways:
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We present a globally convergent gradient-based method for estimating DDC models without the restrictive assumption of linearly parameterized rewards.
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Introducing an ERM-based IRL/DDC framework that does not require explicit state transition probability estimation.
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It suggests the possibility of extension to high-dimensional, infinite state spaces through compatibility with nonparametric estimation techniques such as neural networks.
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Ensuring fast global convergence by satisfying the PL condition of the Bellman residual.
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Synthetic experiments verified superior performance compared to existing methods.
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Limitations:
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Only experimental results on synthetic data are presented, so generalization performance on real datasets requires further verification.
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Further theoretical research is needed to determine whether the PL condition satisfaction is applicable to all DDC problems.
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Scalability in high-dimensional, infinite state spaces has only theoretically suggested potential, but actual implementation and performance evaluation require further research.
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