GP-UCB

A simple implementation of GP-UCB[1]. The main purpose is to provide the overview of GP-UCB algorithm's dynamics.

Usage

Please refer the bottom of gpucb.py for sample settings.

1. Set up a meshgrid which defines the search space.

x = np.arange(-3, 3, 0.25)
y = np.arange(-3, 3, 0.25)
grid = np.meshgrid(x, y)

2. Set up an environment class which is equipped with sample() method.

class DummyEnvironment(object):
    def sample(self, x):
        return np.sin(x[0]) + np.cos(x[1])

3. Create a GPUCB instance by padding the search grid and the environment instance.

env = DummyEnvironment()
agent = GPUCB(grid, env)

4. Iterate learn() method to obtain the estimated curve. If the search space is 2D, you can also use plot() method to visualize the entire situation.

for i in range(20):
    agent.learn()
    agent.plot()

References

1. Srinivas, Niranjan, et al. "Gaussian process optimization in the bandit setting: No regret and experimental design." arXiv preprint arXiv:0912.3995 (2009).