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 . However, in practice model-based methods are unable to achieve the same asymptotic performance on challenging continuous control tasks due to the complexity of learning and controlling an explicit world model .
After 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 in terms of final reward, especially in non-trivial environments. In response, researchers have proposed model-based methods are unable to achieve the same asymptotic performance on challenging continuous control tasks due to the complexity of learning and controlling an explicit world model .