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Reinforcement Learning in VS .NET Maker PDF 417 in VS .NET Reinforcement Learning

11.3. Reinforcement Learning Using Barcode encoder for .NET Control to generate, create PDF 417 image in .NET applications. GTIN-14 1: 2: 3: 4: PDF417 for .NET framework 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18:. controller Q-learning(S, A, , ) Inputs S is a set of states A is a set of actions the discount is the step size Local real array Q[S, A] previous state s previous action a initialize Q[S, A] arbitrarily observe current state s repeat select and carry out an action a observe reward r and state s Q[s, a] Q[s, a] + (r + maxa Q[s , a ] Q[s, a]) s s until termination Figure 11.10: Q-learning controller. Recall (pag PDF 417 for .NET framework e 404) that Q (s, a), where a is an action and s is a state, is the expected value (cumulative discounted reward) of doing a in state s and then following the optimal policy. Q-learning uses temporal differences to estimate the value of Q (s, a).

In Q-learning, the agent maintains a table of Q[S, A], where S is the set of states and A is the set of actions. Q[s, a] represents its current estimate of Q (s, a). An experience s, a, r, s provides one data point for the value of Q(s, a).

The data point is that the agent received the future value of r + V (s ), where V (s ) = maxa Q(s , a ); this is the actual current reward plus the discounted estimated future value. This new data point is called a return. The agent can use the temporal difference equation (11.

1) to update its estimate for Q(s, a): Q[s, a] Q[s, a] + r + max Q[s , a ] Q[s, a]. or, equival ently, Q[s, a] (1 )Q[s, a] + r + max Q[s , a ] .. Figure 11.1 .NET PDF 417 0 shows the Q-learning controller.

This assumes that is xed; if is varying, there will be a different count for each state action pair and the algorithm would also have to keep track of this count.. 11. Beyond Supervised Learning Q-learning .NET PDF 417 learns an optimal policy no matter which policy the agent is actually following (i.e.

, which action a it selects for any state s) as long as there is no bound on the number of times it tries an action in any state (i.e., it does not always do the same subset of actions in a state).

Because it learns an optimal policy no matter which policy it is carrying out, it is called an off-policy method. Example 11.9 Consider the domain Example 11.

7 (page 463), shown in Figure 11.8 (page 464). Here is a sequence of s, a, r, s experiences, and the update, where = 0.

9 and = 0.2, and all of the Q-values are initialized to 0 (to two decimal points):. s s0 s2 s4 s0 s2 s4 s0 s2 s2 s4 a upC up left upC up left up up up left r 1 0 10 1 0 10 0 100 0 10 s s2 s4 s0 s2 s4 s0 s2 s2 s4 s0 Update Q[s0 , upC] = 0.2 Q[s2 , up] = 0 Q[s4 , left] = 2.0 Q[s0 , upC] = 0.

36 Q[s2 , up] = 0.36 Q[s4 , left] = 3.6 Q[s0 , upC] = 0.

06 Q[s2 , up] = 19.65 Q[s2 , up] = 15.07 Q[s4 , left] = 4.

89. Notice how PDF-417 2d barcode for .NET the reward of 100 is averaged in with the other rewards. After the experience of receiving the 100 reward, Q[s2 , up] gets the value 0.

8 0.36 + 0.2 ( 100 + 0.

9 0.36) = 19.65 At the next step, the same action is carried out with a different outcome, and Q[s2 , up] gets the value 0.

8 19.65 + 0.2 (0 + 0.

9 3.6) = 15.07 After more experiences going up from s2 and not receiving the reward of 100, the large negative reward will eventually be averaged in with the positive rewards and eventually have less in uence on the value of Q[s2 , up], until going up in state s2 once again receives a reward of 100.

. It is instr uctive to consider how using k to average the rewards works when the earlier estimates are much worse than more recent estimates. The following example shows the effect of a sequence of deterministic actions. Note that when an action is deterministic we can use = 1.

Example 11.10 Consider the domain Example 11.7 (page 463), shown in Figure 11.

8 (page 464). Suppose that the agent has the experience. s0 , right, PDF417 for .NET framework 0, s1 , upC, 1, s3 , upC, 1, s5 , left, 0, s4 , left, 10, s0 and repeats this sequence of actions a number of times. (Note that a real Qlearning agent would not keep repeating the same actions, particularly when.

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