Abstract

Policy search is a subfield in reinforcement learning which focuses on
finding good parameters for a given policy parametrization. It is well
suited for robotics as it can cope with high-dimensional state and action
spaces, one of the main challenges in robot learning. We review recent
successes of both model-free and model-based policy search in robot
learning.
Model-free policy search is a general approach to learn policies
based on sampled trajectories. We classify model-free methods based on
their policy evaluation strategy, policy update strategy, and exploration
strategy and present a unified view on existing algorithms. Learning a
policy is often easier than learning an accurate forward model, and,
hence, model-free methods are more frequently used in practice. However,
for each sampled trajectory, it is necessary to interact with the
* Both authors contributed equally.
robot, which can be time consuming and challenging in practice. Modelbased
policy search addresses this problem by first learning a simulator
of the robot’s dynamics from data. Subsequently, the simulator generates
trajectories that are used for policy learning. For both modelfree
and model-based policy search methods, we review their respective
properties and their applicability to robotic systems.