January 2018
Intermediate to advanced
470 pages
11h 9m
English
Content preview from Scala Machine Learning Projects
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Evaluating the model
The preceding output shows the transition from one state to another, and for the 0.1 coverage, the QLearning model had 15,750 transitions for 126 states to reach goal state 37 with optimal rewards. Therefore, the training set is quite small and only a few buckets have actual values. So we can understand that the size of the training set has an impact on the number of states. QLearning will converge too fast for a small training set (like what we have for this example).
However, for a larger training set, QLearning will take time to converge; it will provide at least one value for each bucket created by the approximation. Also, by seeing those values, it is difficult to understand the relation between Q-values and states. ...
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