Learning Safety Constraints From Demonstration Using One-Class Decision
  Trees
Mattijs Baert Sam Leroux Pieter Simoens
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.