Relational reinforcement learning

摘要

Reinforcement learning is a subtopic of machine learning that is concerned with software systems that learn to behave through interaction with their environment and receive only feedback on the quality of their current behavior instead of a set of correctly labelled learning examples. Although reinforcement learning algorithms have been studied extensively in a propositional setting, their usefulness in complex problems is limited by their inability to incorporate relational information about the environment. Relational Reinforcement Learning is concerned with reinforcement learning in domains that exhibit structural properties and in which different kinds of related objects exist. These domains are usually characterized by a very large and possibly unbounded number of different possible states and actions. In this kind of environment, most traditional reinforcement learning techniques break down.