A Survey on Policy Search for Robotics

Policy search is a subfield of Reinforcement Learning (RL) that focuses on finding good parameters for a given policy parameterization. It is well suited tor robotics as it can cope with high-dimensional state and action spaces, which is one of the main challenges in robot learning. A Survey on Policy Search for Robotics reviews 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. This text classifies model-free methods based on their policy evaluation, policy update, and exploration strategies, and presents a unified view of existing algorithms. Learning a policy is often easier than…