Learning Safety Constraints From Demonstration Using One-Class Decision Trees
Keywords: constraint inference, inverse constrained reinforcement learning, constrained reinforcement learning, one-class decision tree
TL;DR: We present an approach which employs one-class decision trees to learn constraints, expressed in formal logic, from expert demonstrations.
Abstract: The alignment of autonomous agents with human values is a pivotal challenge when deploying these agents within physical environments, where safety is an important concern. However, defining the agent's objective as a reward and/or cost function is inherently complex and prone to human errors. In response to this challenge, we present a novel approach that leverages one-class decision trees to facilitate learning from expert demonstrations. These decision trees provide a foundation for representing a set of constraints pertinent to the given environment as a logical formula in disjunctive normal form. The learned constraints are subsequently employed within an oracle constrained reinforcement learning framework, enabling the acquisition of a safe policy. In contrast to other methods, our approach offers an interpretable representation of the constraints, a vital feature in safety-critical environments. To validate the effectiveness of our proposed method, we conduct experiments in synthetic benchmark domains and a realistic driving environment.
Submission Number: 4
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