  Inverse reinforcement learning (IRL) is computationally challenging, with
common approaches requiring the solution of multiple reinforcement learning
(RL) sub-problems. This work motivates the use of potential-based reward
shaping to reduce the computational burden of each RL sub-problem. This work
serves as a proof-of-concept and we hope will inspire future developments
towards computationally efficient IRL.
