bayesian_optimization

Overview ¶

Package main demonstrates Bayesian Optimization for hyperparameter tuning.

Bayesian Optimization is a powerful technique for finding optimal hyperparameters efficiently. It uses a probabilistic surrogate model to guide the search, balancing exploration of unknown regions with exploitation of known good configurations.

This example shows:

• Setting up a search space with different parameter types
• Defining an objective function (simulated agent performance)
• Running optimization with different acquisition functions
• Analyzing optimization results and convergence

Run with: go run bayesian_optimization_example.go