LGDec 15, 2023
Toward Computationally Efficient Inverse Reinforcement Learning via Reward ShapingLauren H. Cooke, Harvey Klyne, Edwin Zhang et al.
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.