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Why is this rewrite important Because it shows that if the agent learns the Q function instead of the V * function, it will be able to select optimal actions even when it has no knowledge of thefunctions r and 6 As Equation (135) makes clear, it need only consider each available action a in its current state s and choose the action that maximizes Q(s, a) It may at first seem surprising that one can choose globally optimal action sequences by reacting repeatedly to the local values of Q for the current state This means the agent can choose the optimal action without ever conducting a lookahead search to explicitly consider what state results from the action Part of the beauty of Q learning is that the evaluation function is defined to have precisely this property-the value of Q for the current state and action summarizes in a single number all the information needed to determine the discounted cumulative reward that will be gained in the future if action a is selected in state s To illustrate, Figure 132 shows the Q values for every state and action in the simple grid world Notice that the Q value for each state-action transition equals the r value for this transition plus the V* value for the resulting state discounted by y Note also that the optimal policy shown in the figure corresponds to selecting actions with maximal Q values

I needed to construct this test slightly differently to extract the string values of the float inf that is coming back from the calculation. This comes back as an NSString. So I need to create an NSString containing "inf" and compare the two NSString objects for equality using the STAssertEqualObjects macro. Let s look at the results so far. This time, expand the report to show more detail. You can click on the little transcript icon that appears at the right end of any highlighted line in the Build Results tab. Depending on the level you select, you will see more or less detail. Using the disclosure arrows, find and select the line that starts Run test suite.... Then click on the transcript icon. You will see a detailed report on the suite of tests see Figure 11 9.

Learning the Q function corresponds to learning the optimal policy How can Q be learned The key problem is finding a reliable way to estimate training values for Q, given only a sequence of immediate rewards r spread out over time This can be accomplished through iterative approximation To see how, notice the close relationship between Q and V * ,

= V * ( S ) max Q(s,a')

You can also open the full transcript text file in the code editor. To do this, right-click on a line in the Build Results tab and choose the menu item Open These Latest Results as Transcript Text File.

Q(s,a ) = r(s, a )

The format and design of the following scannable CVs adhere to guidelines established by Joyce Lain Kennedy and Thomas J Morrow in their pioneering publication, Electronic Resume Revolution

+ y max Q ( W ,a ) , a') a'

Seasoned unit testers will be aware that there are other factors to consider in unit testing beyond rightness and boundary conditions. Here are a few: Cross-check: If possible, find more than one way to test an algorithm.

This recursive definition of Q provides the basis for algorithms that iteratively approximate Q (Watkins 1989) To describe the algorithm, we will use the symbol Q to refer to the learner's estimate, or hypothesis, of the actual Q function In this algorithm the learner represents its hypothesis Q by a large table with a separate entry for each state-action pair The table entry for the pair (s, a ) stores the value for ~ ( sae ), h t learner's current hypothesis about the actual but unknown value Q(s, a) The table can be initially filled with random values (though it is easier to understand the algorithm if one assumes initial values of zero) The agent repeatedly observes its current state s, chooses some action a, executes this action, then observes the resulting reward r = r(s, a) and the new state s' = 6(s, a) It then updates the table entry for ~ ( sa ,) following each such transition, according to the rule: Q(S,a )

+ y max &(st,a')