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  3. Deep Reinforcement Learning in Python
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Hands-on with Optuna

Use Optuna to optimize the hyperparameters of a simple function.

In practice, you would want to optimize an objective function which is expensive or time-consuming to evaluate. As a result, you want to find reasonable hyperparameters in as few trials as possible.

For convenience, you will use a predefined objective function here instead which can be evaluated near-instantaneously:

$$f(x,y) = 2*(1-x)^2 + (y-x)^2$$

The metric() function is defined in your environment.

For this exercise, x and y are the hyperparameters that you optimize for.

说明 1 / 共 3 个

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  • Declare the hyperparameters x and y as floats allowed to vary uniformly between -5 and 5.