![]() ![]() The temperature parameter here might be 1/temperature seen elsewhere. Each "action" corresponds to one indexed entry in the vector objects passed around in this code. This is a simple example of using the Softmax function. Asymptotic analysis provides methods for obtaining approximate solutions of problems near a specific value such as 0 or Infinity. The study of limits belongs to the branch of mathematics called asymptotic analysis. A "temperature" parameter allows the selection policy to be tuned, interpolating between pure exploitation (a "greedy" policy, where the highest-weighted action is always chosen) and pure exploration (where each action has an equal probability of being chosen). Using Version 11.2, we can confirm that the limiting value is indeed 2 by requesting the value r() in RSolveValue, as shown here. The Softmax function is commonly used to map output weights to a set of corresponding probabilities. In reenforcement learning, a set of available actions' weights might need to be mapped to a set of associated probabilities, which will then by used to randomly select the next action taken. For some machine learning applications, there is a point where a set of raw outputs (like from a neural network) needs to be mapped to a set of probabilities, normalized to sum to 1. ![]()
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