🛠️ Task: Briefing the Search Specialist (Optuna)

It's time to deploy our automated search specialist. Before Optuna can start its work, it needs a clear briefing. Your task is to define the "search space"—the boundaries of the investigation for each key hyperparameter. In the code cell below, the `objective` function is provided. The main logic for training and evaluation is complete. You only need to **complete the section that defines the hyperparameter search space.** **Task 1:** Fill in the `YOUR CODE HERE` section by calling the correct `trial.suggest_*` method for each of the following parameters: 1. **`learning_rate`:** A logarithmic search space between `1e-5` and `1e-3`. 2. **`buffer_size`:** A discrete list of choices: `1000`, `5000`, `10000`, and `20000`. 3. **`target_update_interval`:** An integer search space from `10` to `2000`. 4. **`exploration_fraction`:** A uniform float search space from `0.1` to `0.8`. 5. **`net_arch`:** A discrete list of choices for the network architecture: `[32, 32]`, `[64, 64]`, and `[128, 128]`.

Click here for the exact syntax

Here is the code you will need to implement: ```python # We tell Optuna what values to try for each parameter. learning_rate = trial.suggest_float("learning_rate", 1e-5, 1e-3, log=True) buffer_size = trial.suggest_categorical("buffer_size", [1000, 5000, 10000, 20000]) target_update_interval = trial.suggest_int("target_update_interval", 10, 2000) exploration_fraction = trial.suggest_float("exploration_fraction", 0.1, 0.8) net_arch = trial.suggest_categorical("net_arch", [[32, 32], [64, 64], [128, 128]]) ```

**Task 2:** Your job is to write the two lines of code that launch the Optuna optimization process. 1. **Create the Study:** You must create an Optuna `study` object. When you create it, you need to tell it whether you want to `maximize` or `minimize` the score from the objective function. 2. **Launch the Optimization:** Call the study's `.optimize()` method, passing it the `objective` function and the number of `n_trials` you want to run.

Click here for syntax hints

You will need to import the `optuna` library first. ```python import optuna # 1. Create the study. Since higher rewards are better, what should the direction be? study = optuna.create_study(direction="maximize") # 2. Call the optimize method. study.optimize(objective, n_trials=40) ```