Chinese Academy of Sciences

Chinese Academy of Sciences

Practical Model-Based Policy Optimization for Efficient Reinforcement Learning

Recent advancements in probabilistic model-based reinforcement learning (MBRL) have accelerated learning by generating samples from the model. However, this approach often suffers from time inefficiencies caused by frequent model updates. To address this issue, researchers from the Chinese Academy of Sciences propose a novel model-based policy optimization (PMBPO) framework that