Maximum Entropy Model Correction in Reinforcement Learning

Amin Rakhsha, Mete Kemertas, Mohammad Ghavamzadeh +1

We propose and theoretically analyze an approach for planning with an approximate model in reinforcement learning that can reduce the adverse impact of model error. If the model is…