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This work handles the inverse reinforcement learning (IRL) problem where onlya small number of demonstrations are available from a demonstrator for eachhigh-dimensional task, insufficient to estimate an accurate reward function.Observing that each demonstrator has an inherent reward for each state and thetask-specific behaviors mainly depend on a small number of key states, wepropose a meta IRL algorithm that first models the reward function for eachtask as a distribution conditioned on a baseline reward function shared by alltasks and dependent only on the demonstrator, and then finds the most likelyreward function in the distribution that explains the task-specific behaviors.We test the method in a simulated environment on path planning tasks withlimited demonstrations, and show that the accuracy of the learned rewardfunction is significantly improved. We also apply the method to analyze themotion of a patient under rehabilitation.

Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis