Strategic Exploration for Inverse Constraint Inference with Efficiency Guarantee

ICLR 2025 Conference Submission6372 Authors

Keywords: Inverse Constrained Reinforcement Learning, Exploration Algorithm, Sample Efficiency

TL;DR: This paper introduces a strategically efficient exploration framework for Inverse Constrained Reinforcement Learning problems with theoretically tractable sample complexity.

Abstract: In many realistic applications, the constraint is not readily available, and we need to infer the constraints respected by the expert agents from their behaviors. The problem is known as Inverse Constraint Inference (ICI). A common solver, Inverse Constrained Reinforcement Learning (ICRL) seeks to recover the optimal constraints in complex environments in a data-driven manner. Existing ICRL algorithms collect training samples from an interactive environment. However, the efficacy and efficiency of these sampling strategies remain unknown. To bridge this gap, we introduce a strategic exploration framework with guaranteed efficiency. Specifically, we define a feasible constraint set for ICRL problems and investigate how expert policy and environmental dynamics influence the optimality of constraints. Motivated by our findings, we propose two exploratory algorithms to achieve efficient constraint inference via 1) dynamically reducing the bounded aggregate error of cost estimation and 2) strategically constraining the exploration policy. Both algorithms are theoretically grounded with tractable sample complexity. We empirically demonstrate the performance of our algorithms under various environments.

Primary Area: reinforcement learning

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Submission Number: 6372