SEMDICE: Improving Off-Policy Reinforcement Learning with Entropy Maximization

The article likely introduces a novel reinforcement learning algorithm, SEMDICE, focusing on off-policy learning and entropy maximization. The core contribution seems to be a method for estimating and correcting the stationary distribution to improve performance.

• •SEMDICE is likely a new reinforcement learning algorithm.
• •The method targets off-policy learning.
• •It uses state entropy maximization with stationary distribution correction.