128 lines
3.8 KiB
Python
128 lines
3.8 KiB
Python
import numpy as np
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from sklearn.gaussian_process import GaussianProcessRegressor
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from sklearn.gaussian_process.kernels import Matern
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from PolicyModel.GaussianModel import GaussianPolicy
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from ToyTask.MountainCar import MountainCarEnv
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from AcquistionFunctions.ExpectedImprovement import ExpectedImprovement
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import matplotlib.pyplot as plt
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from warnings import catch_warnings, simplefilter
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class BayesianOptimization:
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def __init__(self, env, nr_init=3, acq='ei', nr_weights=8, policy_seed=None):
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self.env = env
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self.nr_init = nr_init
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self.acq = acq
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self.X = None
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self.Y = None
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self.gp = None
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self.episode = 0
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self.best_reward = np.empty((1, 1))
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self.nr_policy_weights = nr_weights
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self.nr_steps = env.max_step
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self.policy_seed = policy_seed
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self.lowerb = -1.0
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self.upperb = 1.0
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self.policy_model = GaussianPolicy(self.nr_policy_weights,
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self.nr_steps,
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self.policy_seed,
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self.lowerb,
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self.upperb)
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self.nr_test = 100
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def initialize(self):
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self.X = np.zeros((self.nr_init, self.nr_policy_weights))
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self.Y = np.zeros((self.nr_init, 1))
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for i in range(self.nr_init):
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self.policy_model.random_policy()
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self.X[i, :] = self.policy_model.weights.T
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policy = self.policy_model.policy_rollout()
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reward, step_count = self.env.runner(policy)
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self.Y[i] = reward
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self.gp = GaussianProcessRegressor(Matern(nu=1.5))
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self.gp.fit(self.X, self.Y)
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def next_observation(self):
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if self.acq == 'ei':
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x_next = ExpectedImprovement(self.gp,
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self.X,
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self.nr_test,
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self.nr_policy_weights,
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seed=self.policy_seed,
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lower=self.lowerb,
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upper=self.upperb)
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return x_next
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else:
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raise NotImplementedError
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def eval_new_observation(self, x_next):
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self.policy_model.weights = x_next
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policy = self.policy_model.policy_rollout()
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reward, _ = self.env.runner(policy)
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self.X = np.vstack((self.X, x_next))
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self.Y = np.vstack((self.Y, reward))
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self.gp.fit(self.X, self.Y)
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if self.episode == 0:
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self.best_reward[0] = max(self.Y)
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else:
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self.best_reward = np.vstack((self.best_reward, max(self.Y)))
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self.episode += 1
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def plot(self):
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if self.nr_policy_weights == 1:
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plt.scatter(self.X, self.Y)
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x_plot = np.linspace(self.lowerb, self.upperb, 100).reshape(-1, 1)
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with catch_warnings():
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simplefilter('ignore')
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y_plot, y_sigma = self.gp.predict(x_plot, return_std=True)
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plt.plot(x_plot, y_plot)
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plt.fill_between(
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x_plot.ravel(),
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y_plot - 1.96 * y_sigma,
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y_plot + 1.96 * y_sigma,
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alpha=0.5
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)
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plt.show()
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def plot_reward(self):
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epsiodes = np.linspace(0, self.episode, self.episode)
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plt.plot(epsiodes, self.best_reward)
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plt.show()
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def main():
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max_step = 50
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car = MountainCarEnv(max_step)
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bo = BayesianOptimization(car)
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bo.initialize()
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iteration_steps = 100
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for i in range(iteration_steps):
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x_next = bo.next_observation()
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bo.eval_new_observation(x_next)
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bo.plot_reward()
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bo.policy_model.plot_policy()
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if __name__ == "__main__":
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main()
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