Multi-robot inverse reinforcement learning under occlusion with estimation of state transitions

We relax these assumptions and systematically generalize a known IRL method, Maximum Entropy IRL, to enable the subject to learn the preferences of the patrolling robots, subsequently their behaviors, and predict their future positions well enough to plan a route to its goal state without being spotted. Challenged by occlusion, multiple interacting robots, and partially known dynamics we demonstrate empirically that the generalization improves significantly on several baselines in its ability to inversely learn in this application setting. Of note, it leads to significant improvement in the learner's overall success rate of penetrating the patrols. Our methods represent significant steps towards making IRL pragmatic and applicable to real-world contexts.