If the query point falls within this hull, we perform the prediction as normal. Details of the algorithm are available in a recent paper . Incorporating Prior Knowledge Many reinforcement learning implementations assume that we know nothing about the environment to begin with, and that we must gain all of our information by exploration. When dealing with a real, physical system such as a robot, however, this approach is simply not practical. The state space is typically too large to explore within the lifetime of the robot.
Humans have insight into how to perform many tasks that robots are called on to do.
In this work, we have used the idea of a supplied initial policy. This policy can be either computer code, or a human directing the robot with a joystick. The reinforcement learning component observes the states, actions, next states and rewards being generated and bootstraps this information into its value function representation. It does not, however, have any control over the robot. This allows the system to safely and efficiently bootstrap information into the value function representation.