Gradients can train reward models: An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model

• URL: http://arxiv.org/abs/2502.14131v1
• Date: Wed, 19 Feb 2025 22:22:20 GMT
• Title: Gradients can train reward models: An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model
• Authors: Enoch H. Kang, Hema Yoganarasimhan, Lalit Jain,
• Abstract summary: We study the problem of estimating Dynamic Choice (DDC) models, also known as offline Maximum Entropy-Regularized Inverse Reinforcement Learning ( offline MaxEnt-IRL) in machine learning. The objective is to recover reward or $Q*$ functions that govern agent behavior from offline behavior data. We propose a globally convergent gradient-based method for solving these problems without the restrictive assumption of linearly parameterized rewards.
• Score: 9.531082746970286
• License:
• Abstract: We study the problem of estimating Dynamic Discrete Choice (DDC) models, also known as offline Maximum Entropy-Regularized Inverse Reinforcement Learning (offline MaxEnt-IRL) in machine learning. The objective is to recover reward or $Q^*$ functions that govern agent behavior from offline behavior data. In this paper, we propose a globally convergent gradient-based method for solving these problems without the restrictive assumption of linearly parameterized rewards. The novelty of our approach lies in introducing the Empirical Risk Minimization (ERM) based IRL/DDC framework, which circumvents the need for explicit state transition probability estimation in the Bellman equation. Furthermore, our method is compatible with non-parametric estimation techniques such as neural networks. Therefore, the proposed method has the potential to be scaled to high-dimensional, infinite state spaces. A key theoretical insight underlying our approach is that the Bellman residual satisfies the Polyak-Lojasiewicz (PL) condition -- a property that, while weaker than strong convexity, is sufficient to ensure fast global convergence guarantees. Through a series of synthetic experiments, we demonstrate that our approach consistently outperforms benchmark methods and state-of-the-art alternatives.

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