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: For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to agents in hopes of improving the sample-efficiency in comparison to model-free agents .