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has or does not have prior knowledge about the effects of its actions on the environment Note we have touched on the problem of learning to control sequential processes earlier in this book In Section 114 we discussed explanation-based learning of rules to control search during problem solving There the problem is for the agent to choose among alternative actions at each step in its search for some goal state The techniques discussed here differ from those of Section 114, in that here we consider problems where the actions may have nondeterministic outcomes and where the learner lacks a domain theory that describes the outcomes of its actions In 1 we discussed the problem of learning to choose actions while playing the game of checkers There we sketched the design of a learning method very similar to those discussed in this chapter In fact, one highly successful application of the reinforcement learning algorithms of this chapter is to a similar program, which game-playing problem Tesauro (1995) describes the TD-GAMMON has used reinforcement learning to become a world-class backgammon player This program, after training on 15 million self-generated games, is now considered nearly equal to the best human players in the world and has played competitively against top-ranked players in international backgammon tournaments The problem of learning a control policy to choose actions is similar in some respects to the function approximation problems discussed in other chapters The target function to be learned in this case is a control policy, n : S + A, that outputs an appropriate action a from the set A , given the current state s from the set S However, this reinforcement learning problem differs from other function approximation tasks in several important respects

Coloring the outside edge of a DVD with a green marker (or any other color) makes no difference in video or audio quality Data is read based on pit interference at one-quarter of the laser wavelength, a distance of less than 165 nanometers A bit of dye that on average is more than 3 million times farther away is not going to affect anything

(continued)

.

Delayed reward The task of the agent is to learn a target function n that maps from the current state s to the optimal action a = n(s) In earlier chapters we have always assumed that when learning some target function such as n, each training example would be a pair of the form (s,n ( s ) ) In reinforcement learning, however, training information is not available in this form Instead, the trainer provides only a sequence of immediate reward values as the agent executes its sequence of actions The agent, therefore, faces the problem of temporal credit assignment: determining which of the actions in its sequence are to be credited with producing the eventual rewards Exploration In reinforcement learning, the agent influences the distribution of training examples by the action sequence it chooses This raises the question of which experimentation strategy produces most effective learning The learner faces a tradeoff in choosing whether to favor exploration of unknown states and actions (to gather new information), or exploitation of states and actions that it has already learned will yield high reward (to maximize its cumulative reward) Partially observable states Although it is convenient to assume that the agent's sensors can perceive the entire state of the environment at each time step, in many practical situations sensors provide only partial information For example, a robot with a forward-pointing camera cannot see what is

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If you notice problems when playing a disc, you may be able to correct them with a simple cleaning Do not use strong cleaners, abrasives, solvents, or acids With a soft, lint-free cloth, wipe gently in only a radial direction (in a straight line between the hub and the rim) Since the data is arranged circularly on the disc, the microscratches you create when cleaning the disc will cross more error correction blocks and be less likely to cause unrecoverable errors Do not use canned or compressed air, which can be very cold from rapid expansion and may stress the disc thermally For stubborn dirt or gummy adhesive, use water, water with mild soap, or isopropyl alcohol As a last resort, try peanut oil Let it sit for about a minute before wiping it off Commercial products are available to clean discs, and they provide some protection from dust, fingerprints, and scratches Cleaning products labeled for use on CDs work as well as those that say they are for DVDs If you continue to have problems after cleaning the disc, you may need to attempt to repair one or more scratches Sometimes even hairline scratches can cause errors if they just happen to cover an entire ECC block Examine the disc, keeping in mind that the laser reads from the bottom There are essentially two methods of repairing scratches: (1) fill or coat the scratch with an optical material or (2) polish down the scratch Many commercial products do one or both of these, or you may wish to buy polishing compounds or toothpaste and do it yourself The trick is to polish out the scratch without causing new ones A mess of small polishing scratches can cause more damage than a big scratch As with cleaning, polish only in the radial direction Libraries, rental shops, and other venues that need to clean many discs may want to invest in a commercial polishing machine that can restore a disc to pristine condition after an amazing amount of abuse Keep in mind that the data layer on a DVD is only half as deep as on a CD, so a DVD can only be repolished about half as many times.

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