 In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respectingunknown constraints , we propose a framework to learn the most likely constraints that the expert respects.
<meaning-changed>  In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respectingunknown constraints , we propose a framework to learn the most likely constraints that the expert respects.
 In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respectingunknown we consider the problem of learning constraints from demonstrations of a constraint-abiding agent's behavior. We experimentally validate our approach and show that our framework can successfully learn the most likely constraints that the expert respects.