  Conventionally, model-based reinforcement learning (MBRL) aims to learn a
global model for the dynamics of the environment. A good model can potentially
enable planning algorithms to generate a large variety of behaviors and solve
diverse tasks. However, learning an accurate model for complex dynamical
systems is difficult, and even then, the model might not generalize well
outside the distribution of states on which it was trained. In this work, we
combine model-based learning with model-free learning of primitives that make
model-based planning easy. To that end, we aim to answer the question: how can
we discover skills whose outcomes are easy to predict? We propose an
unsupervised learning algorithm, Dynamics-Aware Discovery of Skills (DADS),
which simultaneously discovers predictable behaviors and learns their dynamics.
Our method can leverage continuous skill spaces, theoretically, allowing us to
learn infinitely many behaviors even for high-dimensional state-spaces. We
demonstrate that zero-shot planning in the learned latent space significantly
outperforms standard MBRL and model-free goal-conditioned RL, can handle
sparse-reward tasks, and substantially improves over prior hierarchical RL
methods for unsupervised skill discovery.
