Feature: Add Bayesian hyperparameter optimization

[ADGEfficiency]

Description

This PR adds Bayesian hyperparameter optimization for the energy-py library using Optuna.

Background

Hyperparameter optimization is a crucial step in machine learning model development. This implementation uses Optuna, a state-of-the-art hyperparameter optimization framework, to systematically search the hyperparameter space and find optimal configurations for the battery environment RL agent.

Changes

• Add a new example script for Bayesian hyperparameter optimization
• Implement optimization of key RL algorithm parameters (learning rate, batch size, gamma, etc.)
• Include neural network architecture in the optimization process
• Generate visualization artifacts during the optimization process
• Train a final model with the best found hyperparameters