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2.12 Bibliographical and Historical Remarks


Bandit problems have been studied in statistics, engineering, and psychology. In statistics, bandit problems fall under the heading "sequential design of experiments," introduced by Thompson (1933, 1934) and Robbins (1952), and studied by Bellman (1956). Berry and Fristedt (1985) provide an extensive treatment of bandit problems from the perspective of statistics. Narendra and Thathachar (1989) treat bandit problems from the engineering perspective, providing a good discussion of the various theoretical traditions that have focused on them. In psychology, bandit problems have played roles in statistical learning theory (e.g., Bush and Mosteller, 1955; Estes, 1950).

The term greedy is often used in the heuristic search literature (e.g., Pearl, 1984). The conflict between exploration and exploitation is known in control engineering as the conflict between identification (or estimation) and control (e.g., Witten, 1976). Feldbaum (1965) called it the dual control problem, referring to the need to solve the two problems of identification and control simultaneously when trying to control a system under uncertainty. In discussing aspects of genetic algorithms, Holland (1975) emphasized the importance of this conflict, referring to it as the conflict between the need to exploit and the need for new information.


Action-value methods for our -armed bandit problem were first proposed by Thathachar and Sastry (1985). These are often called estimator algorithms in the learning automata literature. The term action value is due to Watkins (1989). The first to use $\varepsilon $-greedy methods may also have been Watkins (1989, p. 187), but the idea is so simple that some earlier use seems likely.


The term softmax for the action selection rule (2.2) is due to Bridle (1990). This rule appears to have been first proposed by Luce (1959). The parameter is called temperature in simulated annealing algorithms (Kirkpatrick, Gelatt, and Vecchi, 1983).


The main argument and results in this section were first presented by Sutton (1984). Further analysis of the relationship between evaluation and instruction has been presented by Barto (1985, 1991, 1992), and Barto and Anandan (1985). The unit-square representation of a binary bandit task used in Figure  2.2 has been called a contingency space in experimental psychology (e.g., Staddon, 1983).

Narendra and Thathachar (1989) provide a comprehensive treatment of modern learning automata theory and its applications. They also discuss similar algorithms from the statistical learning theory of psychology. Other methods based on converting reinforcement learning experience into target actions were developed by Widrow, Gupta, and Maitra (1973) and by Gällmo and Asplund (1995).


This material falls under the general heading of stochastic iterative algorithms, which is well covered by Bertsekas and Tsitsiklis (1996).


Reinforcement comparison methods were extensively developed by Sutton (1984) and further refined by Williams (1986, 1992), Kaelbling (1993a), and Dayan (1991). These authors analyzed many variations of the idea, including other eligibility terms that may significantly improve performance. Perhaps the earliest use of reinforcement comparison was by Barto, Sutton, and Brouwer (1981).


The pursuit algorithm is due to Thathachar and Sastry (1985).


The term associative search and the corresponding problem were introduced by Barto, Sutton, and Brouwer (1981). The term associative reinforcement learning has also been used for associative search (Barto and Anandan, 1985), but we prefer to reserve that term as a synonym for the full reinforcement learning problem (as in Sutton, 1984). We note that Thorndike's Law of Effect (quoted in Chapter 1) describes associative search by referring to the formation of associative links between situations (states) and actions. According to the terminology of operant, or instrumental, conditioning (e.g., Skinner, 1938), a discriminative stimulus is a stimulus that signals the presence of a particular reinforcement contingency. In our terms, different discriminative stimuli correspond to different states.


Interval estimation methods are due to Lai (1987) and Kaelbling (1993a). Bellman (1956) was the first to show how dynamic programming could be used to compute the optimal balance between exploration and exploitation within a Bayesian formulation of the problem. The survey by Kumar (1985) provides a good discussion of Bayesian and non-Bayesian approaches to these problems. The term information state comes from the literature on partially observable MDPs; see, e.g., Lovejoy (1991). The Gittins index approach is due to Gittins and Jones (1974). Duff (1995) showed how it is possible to learn Gittins indices for bandit problems through reinforcement learning.

next up previous contents
Next: 3. The Reinforcement Learning Up: 2. Evaluative Feedback Previous: 2.11 Conclusions   Contents
Mark Lee 2005-01-04