rdlc data matrix

rdlc data matrix

rdlc data matrix

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1977, and the number grew rapidly to 27,000 in the late 1990s Direct sales to customers, known as sellthrough, began in 1980 Today, the home video market is a $16 billion business in the United States alone Over 87 percent of households have at least one VCR, creating a market of over a hundred million customers who, in 1996, rented about 3 billion tapes and bought over 580 million tapes There are now over 25,000 VHS titles available in the United States, compared with 9,000 on laserdisc In the late 1980s, a new video recording format based on 8-mm tape with metal particles was introduced The reduced size and improved quality were not sufficient to displace the well-established VHS format, but 8-mm and Hi-8 tapes were quite successful in the camcorder market, where smaller size is more significant Around this time, minor improvements were made to television, with stereo sound added in the United States in 1985 and, later, closed captions In 1987, JVC introduced an improved Super VHS system called S-VHS Despite being compatible with VHS and almost doubling the picture quality, S-VHS was never much of a success because there were too many barriers to customer acceptance Players and tapes were much more expensive, VHS tapes worked in S-VHS players but not vice versa A special cable and an expensive TV with an s-video connector were required to take advantage of the improved picture, and S-VHS was not a step toward high-definition television (HDTV), which was receiving lavish attention in the late 1980s and was expected to appear shortly It is interesting to note that DVD has many of the same strikes against it, even the specter of HDTV again The major contribution of S-VHS to the industry was the popularization of the s-video connector, which carried better-quality signals than the standard composite format Svideo connectors are still mistakenly referred to as S-VHS connectors.

(continued)

Kiosks and public learning stations Point-of-purchase displays Museums Video walls and public exhibits

Perhaps the most constraining assumption in our treatment of Q learning up to this point is that the target function is represented as an explicit lookup table, with a distinct table entry for every distinct input value (ie, state-action pair) Thus, the algorithms we discussed perform a kind of rote learning and make no attempt to estimate the Q value for unseen state-action pairs by generalizing from those that have been seen This rote learning assumption is reflected in the convergence proof, which proves convergence only if every possible state-action pair is visited (infinitely often!) This is clearly an unrealistic assumption in large or infinite spaces, or when the cost of executing actions is high As a result, more practical systems often combine function approximation methods discussed in other chapters with the Q learning training rules described here It is easy to incorporate function approximation algorithms such as BACKPROPAGATION into the Q learning algorithm, by substituting a neural network for the lookup table and using each ~ ( sa) update as a training example For example, , we could encode the state s and action a as network inputs and train the network to output the target values of Q given by the training rules of Equations (137) and (1310) An alternative that has sometimes been found to be more successful in practice is to train a separate network for each action, using the state as input and Q as output Another common alternative -is to train one network with the state as input, but with one Q output for each action Recall that in 1, we discussed approximating an evaluation function over checkerboard states using a linear function and the LMS algorithm In practice, a number of successful reinforcement learning systems have been developed by incorporating such function approximation algorithms in place of the lookup table Tesauro's successful TD-GAMMON program for playing backgammon used a neural network and the BACKPROPAGATION algorithm together with a TD(A) training rule Zhang and Dietterich (1996) use a similar combination of BACKPROPAGATION and TD(h) for job-shop scheduling tasks Crites and Barto (1996) describe.

Listing 4-7

Vocational skills training Corporate presentations and communications House video in a store, bar, or dance club Theme park and amusement park exhibits Closed-circuit television Video simulation and video-based training Tourism video on buses, trains, and boats Hotel video channels Media literacy

a neural network reinforcement learning approach for an elevator scheduling task Thrun (1996) reports a neural network based approach to Q learning to learn basic control procedures for a mobile robot with sonar and camera sensors Mahadevan and Connell (1991) describe a Q learning approach based on clustering states, applied to a simple mobile robot control problem Despite the success of these systems, for other tasks reinforcement learning fails to converge once a generalizing function approximator is introduced Examples of such problematic tasks are given by Boyan and Moore (1995), Baird (1995), and Gordon (1995) Note the convergence theorems discussed earlier in this chapter apply only when Q is represented by an explicit table To see the difficulty, consider using a neural network rather than an explicit table to represent Q Note if the learner updates the network to better fit the training Q value for a particular transition (si,ai), the altered network weights may also change the Q estimates for arbitrary other transitions Because these weight changes may increase the error in Q estimates for these other transitions, the argument proving the original theorem no longer holds Theoretical analyses of reinforcement learning with generalizing function approximators are given by Gordon (1995) and Tsitsiklis (1994) Baird (1995) proposes gradient-based methods that circumvent this difficulty by directly minimizing the sum of squared discrepancies in estimates between adjacent states (also called Bellman residual errors)