add initial objective values to initial state for sample efficiency (#2365)

Summary:
<!--
Thank you for sending the PR! We appreciate you spending the time to make BoTorch better.

Help us understand your motivation by explaining why you decided to make this change.

You can learn more about contributing to BoTorch here: https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md
-->

## Motivation

Thank you for providing TuRBO as a small tutorial! In the notebook, however, the initial Y values are ignored in the TurboState which seems data inefficient. As this notebook is the starting point for multiple people if they want to compare against or use TuRBO, I believe this should be fixed.

I document my change below as the diff with notebooks is not super readable. :)

Old implementation (from https://botorch.org/tutorials/turbo_1):
```python
X_turbo = get_initial_points(dim, n_init)
Y_turbo = torch.tensor(
    [eval_objective(x) for x in X_turbo], dtype=dtype, device=device
).unsqueeze(-1)

state = TurboState(dim, batch_size=batch_size)
```
Crucially, when updating the `TurboState`, only the most recent Y values (`Y_next`) are considered and, therefore, the initial points are discarded:
```python
def update_state(state, Y_next):
    ...
    state.best_value = max(state.best_value, max(Y_next).item())
```

Long story short: I changed the initialization of the state to also include the initial Y values:
```python
X_turbo = get_initial_points(dim, n_init)
Y_turbo = torch.tensor(
    [eval_objective(x) for x in X_turbo], dtype=dtype, device=device
).unsqueeze(-1)

state = TurboState(dim, batch_size=batch_size, best_value=max(Y_turbo).item())
```
Interestingly, this also (ever so slightly :D ) improves the final performance of TuRBO :)

**OLD**:
<img width="906" alt="image" src="https://github.com/pytorch/botorch/assets/49341051/d8798eef-567e-4dd7-90ab-4ce9eb69a266">

**NEW**:
<img width="904" alt="image" src="https://github.com/pytorch/botorch/assets/49341051/ccead966-8b51-4980-91e6-14430ac3e743">

 ---
I also added warning supression to the imports to hide my path--I hope that this is ok (I think I also saw this in other notebooks), but I can of course also change this back

Old first code cell:
```python
import os
import math
from dataclasses import dataclass

import torch
from botorch.acquisition import qExpectedImprovement, qLogExpectedImprovement
from botorch.fit import fit_gpytorch_mll
from botorch.generation import MaxPosteriorSampling
from botorch.models import SingleTaskGP
from botorch.optim import optimize_acqf
from botorch.test_functions import Ackley
from botorch.utils.transforms import unnormalize
from torch.quasirandom import SobolEngine

import gpytorch
from gpytorch.constraints import Interval
from gpytorch.kernels import MaternKernel, ScaleKernel
from gpytorch.likelihoods import GaussianLikelihood
from gpytorch.mlls import ExactMarginalLogLikelihood
from gpytorch.priors import HorseshoePrior

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.double
SMOKE_TEST = os.environ.get("SMOKE_TEST")
```

New:
```python
import os
import math
import warnings
from dataclasses import dataclass

import torch
from botorch.acquisition import qExpectedImprovement, qLogExpectedImprovement
from botorch.exceptions import BadInitialCandidatesWarning
from botorch.fit import fit_gpytorch_mll
from botorch.generation import MaxPosteriorSampling
from botorch.models import SingleTaskGP
from botorch.optim import optimize_acqf
from botorch.test_functions import Ackley
from botorch.utils.transforms import unnormalize
from torch.quasirandom import SobolEngine

import gpytorch
from gpytorch.constraints import Interval
from gpytorch.kernels import MaternKernel, ScaleKernel
from gpytorch.likelihoods import GaussianLikelihood
from gpytorch.mlls import ExactMarginalLogLikelihood

warnings.filterwarnings("ignore", category=BadInitialCandidatesWarning)
warnings.filterwarnings("ignore", category=RuntimeWarning)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.double
SMOKE_TEST = os.environ.get("SMOKE_TEST")
```

### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?

Yes.

Pull Request resolved: #2365

Test Plan:
I believe this minor change does not need a test plan.

## Related PRs

PR does not change functionality.

Reviewed By: saitcakmak

Differential Revision: D58377054

Pulled By: sdaulton

fbshipit-source-id: 2243ac3941ae789b7babb7daf9e3ac816e36cafd

Author: brunzema