One major difference between reinforcement learning and supervised learning is that a reinforcement-learner must explicitly explore its environment. In order to highlight the problems of exploration, we treat a very simple case in this section. The fundamental issues and approaches described here will, in many cases, transfer to the more complex instances of reinforcement learning discussed later in the paper.
The simplest possible reinforcement-learning problem is known as the k-armed bandit problem, which has been the subject of a great deal of study in the statistics and applied mathematics literature . The agent is in a room with a collection of k gambling machines (each called a ``one-armed bandit'' in colloquial English). The agent is permitted a fixed number of pulls, h. Any arm may be pulled on each turn. The machines do not require a deposit to play; the only cost is in wasting a pull playing a suboptimal machine. When arm i is pulled, machine i pays off 1 or 0, according to some underlying probability parameter , where payoffs are independent events and the s are unknown. What should the agent's strategy be?
This problem illustrates the fundamental tradeoff between exploitation and exploration. The agent might believe that a particular arm has a fairly high payoff probability; should it choose that arm all the time, or should it choose another one that it has less information about, but seems to be worse? Answers to these questions depend on how long the agent is expected to play the game; the longer the game lasts, the worse the consequences of prematurely converging on a sub-optimal arm, and the more the agent should explore.
There is a wide variety of solutions to this problem. We will consider a representative selection of them, but for a deeper discussion and a number of important theoretical results, see the book by Berry and Fristedt . We use the term ``action'' to indicate the agent's choice of arm to pull. This eases the transition into delayed reinforcement models in Section 3. It is very important to note that bandit problems fit our definition of a reinforcement-learning environment with a single state with only self transitions.
Section 2.1 discusses three solutions to the basic one-state bandit problem that have formal correctness results. Although they can be extended to problems with real-valued rewards, they do not apply directly to the general multi-state delayed-reinforcement case. Section 2.2 presents three techniques that are not formally justified, but that have had wide use in practice, and can be applied (with similar lack of guarantee) to the general case.