Multi-Robot Inverse Reinforcement Learning Under Occlusion with State Transition Estimation

Multi-robot inverse reinforcement learning (mIRL) is broadly useful for learning, from passive observations, the behaviors of multiple robots executing fixed trajectories and interacting with each other. In this paper, we relax a crucial assumption in IRL to make it better suited for wider robotic applications: we allow the transition functions of other robots to be stochastic and do not assume that the transition error probabilities are known to the learner. Challenged by occlusion where large portions of others' state spaces are fully hidden, we present a new approach that maps stochastic transitions to distributions over features.