Before revision: Model-based reinforcement learning approaches add explicit domain knowledge to agents in hopes of improving the sample-efficiency in comparison to model-free agents .
After revision: Model-based reinforcement learning approaches add explicit domain knowledge to agents in hopes of improving the sample-efficiency of reinforcement learning agents. While model-based agents are conceptually appealing, their policies tend to lag behind those of model-free agents .