  Data-driven model predictive control has two key advantages over model-free
methods: a potential for improved sample efficiency through model learning, and
better performance as computational budget for planning increases. However, it
is both costly to plan over long horizons and challenging to obtain an accurate
model of the environment. In this work, we combine the strengths of model-free
and model-based methods. We use a learned task-oriented latent dynamics model
for local trajectory optimization over a short horizon, and use a learned
terminal value function to estimate long-term return, both of which are learned
jointly by temporal difference learning. Our method, TD-MPC, achieves superior
sample efficiency and asymptotic performance over prior work on both state and
image-based continuous control tasks from DMControl and Meta-World. Code and
video results are available at https://nicklashansen.github.io/td-mpc.

Machine Learning;Robotics