  Significant progress has been made in the area of model-based reinforcement
learning. State-of-the-art algorithms are now able to match the asymptotic
performance of model-free methods while being significantly more data
efficient. However, this success has come at a price: state-of-the-art
model-based methods require significant computation interleaved with data
collection, resulting in run times that take days, even if the amount of agent
interaction might be just hours or even minutes. When considering the goal of
learning in real-time on real robots, this means these state-of-the-art
model-based algorithms still remain impractical. In this work, we propose an
asynchronous framework for model-based reinforcement learning methods that
brings down the run time of these algorithms to be just the data collection
time. We evaluate our asynchronous framework on a range of standard MuJoCo
benchmarks. We also evaluate our asynchronous framework on three real-world
robotic manipulation tasks. We show how asynchronous learning not only speeds
up learning w.r.t wall-clock time through parallelization, but also further
reduces the sample complexity of model-based approaches by means of improving
the exploration and by means of effectively avoiding the policy overfitting to
the deficiencies of learned dynamics models.
