  Data efficiency and robustness to task-irrelevant perturbations are
long-standing challenges for deep reinforcement learning algorithms. Here we
introduce a modular approach to addressing these challenges in a continuous
control environment, without using hand-crafted or supervised information. Our
Curious Object-Based seaRch Agent (COBRA) uses task-free intrinsically
motivated exploration and unsupervised learning to build object-based models of
its environment and action space. Subsequently, it can learn a variety of tasks
through model-based search in very few steps and excel on structured hold-out
tests of policy robustness.
