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Taking a look back over the four tests you have written so far, you will see that in each case you are duplicating some code. In each case you instantiate a new Converter, and at the end of each you release the object. OCUnit provides you with some shortcuts here. Unit tests that are designed to run as a suite can have setUp and tearDown methods, which, as the names suggest, do any initial preparation before the tests, and follow those tests with the required cleanup. So let s revisit the code in TCTests.m to improve its efficiency. Add the instantiation of testTempConverter to the setUp method, and release it in the tearDown method. Then remove those steps from each of the individual tests (see Listing 11 8 this shows just the first test since you will change the others in the same way).

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To illustrate the operation of the Q learning algorithm, consider a single action taken by an agent, and the corresponding refinement to Q shown in Figure 133 In this example, the agent moves one cell to the right in its grid world and receives an immediate reward of zero for this transition It then applies the training rule of Equation (137) to refine its estimate Q for the state-action transition it just executed According to the training rule, the new Q estimate for this transition is the sum of the received reward (zero) and the highest Q value associated with the resulting state (loo), discounted by y (9) Each time the agent moves forward from an old state to a new one, Q learning propagates Q estimates backward from the new state to the old At the same time, the immediate reward received by the agent for the transition is used to augment these propagated values of Q Consider applying this algorithm to the grid world and reward function shown in Figure 132, for which the reward is zero everywhere, except when entering the goal state Since this world contains an absorbing goal state, we will assume that training consists of a series of episodes During each episode, the agent begins at some randomly chosen state and is allowed to execute actions until it reaches the absorbing goal state When it does, the episode ends and

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Initial state: S]

1992 1994

Listing 11 8. Adding setUp and tearDown Methods - (void) setUp { testTempConverter = Converter.new; } - (void) tearDown { testTempConverter = nil; } - (void)test25ShouldConvertTo77 { // testTempConverter = Converter.new; removed from here [testTempConverter setValue:[NSNumber numberWithFloat:25.0] forKey:@"originalTemp"]; NSNumber *newTemperatureInF = [NSNumber numberWithFloat:(float)[testTempConverter convertCToF]]; STAssertEquals(77.0f, [newTemperatureInF floatValue], @"Expecting 77.0; we got %f", [newTemperatureInF floatValue]); } [...] @end

Next state:

FIGURE 133 The update to Q after executing a single^ action The diagram on the left shows the initial state s of the robot (R) and several relevant Q values in its initial hypothesis For example, the value ! Q(s1, aright) = 729, where aright refers to the action that moves R to its right When the robot executes the action a r i g h t , it receives immediate reward r = 0 and transitions to state s2 It then updates its estimate i)(sl, a r i g h tbased on its Q estimates for the new state s2 Here y = 09 )

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The chances are that the level of unit testing that I have covered here will provide most, if not all, of what you need to build this methodology into your work pattern. However, I have only scratched the surface of what is possible with unit testing. If you have used other languages, such as Java, Ruby on Rails, or Windows .NET, then you may have come across other features such as fixtures and mock objects as ways of providing alternative input mechanisms for your tests. These involve significantly more planning, though they may be worth investigation if you are building a large system that has complex data processing. You may also be aware of automated building as an approach to software engineering. This approach combines a code base, usually using a Subversion or other repository, that is regularly updated from the working copies of developers, with a process for automatically building the software against the suites of unit tests. This build process may be run as frequently as the team needs it (though daily builds are a common practice) and the report that comes back from the build informs the work of the team for the following day. This is a highly disciplined approach to software engineering and one that is unlikely to appeal to the singleton developer, but, in conjunction with some of the other methods described in this book (notably Agile Development), it can work well in large teams. There is no real infrastructure within Xcode for doing automated builds, but there is some coverage on the Internet (take a look at this resource, for example: http://gusmueller.com/blog/archives/2007/03/me_on_late_night_cocoa.html).

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