  We propose an architecture for learning complex controllable behaviors by
having simple Policies Modulate Trajectory Generators (PMTG), a powerful
combination that can provide both memory and prior knowledge to the controller.
The result is a flexible architecture that is applicable to a class of problems
with periodic motion for which one has an insight into the class of
trajectories that might lead to a desired behavior. We illustrate the basics of
our architecture using a synthetic control problem, then go on to learn
speed-controlled locomotion for a quadrupedal robot by using Deep Reinforcement
Learning and Evolutionary Strategies. We demonstrate that a simple linear
policy, when paired with a parametric Trajectory Generator for quadrupedal
gaits, can induce walking behaviors with controllable speed from 4-dimensional
IMU observations alone, and can be learned in under 1000 rollouts. We also
transfer these policies to a real robot and show locomotion with controllable
forward velocity.
