  A general-purpose intelligent robot must be able to learn autonomously and be
able to accomplish multiple tasks in order to be deployed in the real world.
However, standard reinforcement learning approaches learn separate
task-specific policies and assume the reward function for each task is known a
priori. We propose a framework that learns event cues from off-policy data, and
can flexibly combine these event cues at test time to accomplish different
tasks. These event cue labels are not assumed to be known a priori, but are
instead labeled using learned models, such as computer vision detectors, and
then `backed up' in time using an action-conditioned predictive model. We show
that a simulated robotic car and a real-world RC car can gather data and train
fully autonomously without any human-provided labels beyond those needed to
train the detectors, and then at test-time be able to accomplish a variety of
different tasks. Videos of the experiments and code can be found at
https://github.com/gkahn13/CAPs
