AAAI Publications, Thirtieth AAAI Conference on Artificial Intelligence

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Distance Minimization for Reward Learning from Scored Trajectories
Benjamin Burchfiel, Carlo Tomasi, Ronald Parr

Last modified: 2016-03-05


Many planning methods rely on the use of an immediate reward function as a portable and succinct representation of desired behavior. Rewards are often inferred from demonstrated behavior that is assumed to be near-optimal. We examine a framework, Distance Minimization IRL (DM-IRL), for learning reward functions from scores an expert assigns to possibly suboptimal demonstrations. By changing the expert’s role from a demonstrator to a judge, DM-IRL relaxes some of the assumptions present in IRL, enabling learning from the scoring of arbitrary demonstration trajectories with unknown transition functions. DM-IRL complements existing IRL approaches by addressing different assumptions about the expert. We show that DM-IRL is robust to expert scoring error and prove that finding a policy that produces maximally informative trajectories for an expert to score is strongly NP-hard. Experimentally, we demonstrate that the reward function DM-IRL learns from an MDP with an unknown transition model can transfer to an agent with known characteristics in a novel environment, and we achieve successful learning with limited available training data.


Reinforcement Learning; Robotics; Learning from Demonstration; Inverse Reinforcement Learning; Inverse Optimal Control; IRL; IOC; RL

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