Reinforcement Learning Applications

  One reason that reinforcement learning is popular is that is serves as a theoretical tool for studying the principles of agents learning to act. But it is unsurprising that it has also been used by a number of researchers as a practical computational tool for constructing autonomous systems that improve themselves with experience. These applications have ranged from robotics, to industrial manufacturing, to combinatorial search problems such as computer game playing.

Practical applications provide a test of the efficacy and usefulness of learning algorithms. They are also an inspiration for deciding which components of the reinforcement learning framework are of practical importance. For example, a researcher with a real robotic task can provide a data point to questions such as:

• How important is optimal exploration? Can we break the learning period into exploration phases and exploitation phases?
• What is the most useful model of long-term reward: Finite horizon? Discounted? Infinite horizon?
• How much computation is available between agent decisions and how should it be used?
• What prior knowledge can we build into the system, and which algorithms are capable of using that knowledge?

Let us examine a set of practical applications of reinforcement learning, while bearing these questions in mind.

• Game Playing
• Robotics and Control