  Recent success in deep reinforcement learning for continuous control has been
dominated by model-free approaches which, unlike model-based approaches, do not
suffer from representational limitations in making assumptions about the world
dynamics and model errors inevitable in complex domains. However, they require
a lot of experiences compared to model-based approaches that are typically more
sample-efficient. We propose to combine the benefits of the two approaches by
presenting an integrated approach called Curious Meta-Controller. Our approach
alternates adaptively between model-based and model-free control using a
curiosity feedback based on the learning progress of a neural model of the
dynamics in a learned latent space. We demonstrate that our approach can
significantly improve the sample efficiency and achieve near-optimal
performance on learning robotic reaching and grasping tasks from raw-pixel
input in both dense and sparse reward settings.
