Learning Skills in Reinforcement Learning Using Relative Novelty
We present a method for automatically creating a set of useful temporally-extended actions, or skills, in reinforcement learning. Our method identifies states that allow the agent to transition to a different region of the state space—for example, a doorway between two rooms—and generates temporally-extended actions that efficiently take the agent to these states. In identifying such states we use the concept of relative novelty, a measure of how much short-term novelty a state introduces to the agent. The resulting algorithm is simple, has low computational complexity, and is shown to improve performance in a number of problems.