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added more Acquisition functions

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This commit is contained in:
Niko Feith 2023-02-06 15:43:30 +01:00
parent 1f4f878783
commit 78eccba14b
4 changed files with 119 additions and 16 deletions
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14
AcquistionFunctions/ConfidenceBound.py Normal file
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@ -0,0 +1,14 @@
import numpy as np
from scipy.stats import norm
def ConfidenceBound(gp, X, nr_test, nr_weights, lam=1.2, seed=None, lower=-1.0, upper=1.0):
y_hat = gp.predict(X)
best_y = max(y_hat)
rng = np.random.default_rng(seed=seed)
X_test = rng.uniform(lower, upper, (nr_test, nr_weights))
mu, sigma = gp.predict(X_test, return_std=True)
cb = mu + lam * sigma
idx = np.argmax(cb)
X_next = X_test[idx, :]
return X_next

15
AcquistionFunctions/ProbabilityOfImprovement.py Normal file
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@ -0,0 +1,15 @@
import numpy as np
from scipy.stats import norm
def ProbabilityOfImprovement(gp, X, nr_test, nr_weights, kappa=2.576, seed=None, lower=-1.0, upper=1.0):
y_hat = gp.predict(X)
best_y = max(y_hat)
rng = np.random.default_rng(seed=seed)
X_test = rng.uniform(lower, upper, (nr_test, nr_weights))
mu, sigma = gp.predict(X_test, return_std=True)
z = (mu - best_y - kappa) / sigma
pi = norm.cdf(z)
idx = np.argmax(pi)
X_next = X_test[idx, :]
return X_next

94
BayesianOptimization/BOwithGym.py
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@ -4,6 +4,8 @@ from sklearn.gaussian_process.kernels import Matern
from PolicyModel.GaussianModel import GaussianPolicy
from AcquistionFunctions.ExpectedImprovement import ExpectedImprovement
from AcquistionFunctions.ProbabilityOfImprovement import ProbabilityOfImprovement
from AcquistionFunctions.ConfidenceBound import ConfidenceBound
from ToyTask.MountainCarGym import Continuous_MountainCarEnv
@ -12,6 +14,7 @@ import time
import matplotlib.pyplot as plt
class BayesianOptimization:
def __init__(self, env, nr_step, nr_init=3, acq='ei', nr_weights=6, policy_seed=None):
self.env = env
@ -19,11 +22,12 @@ class BayesianOptimization:
self.acq = acq
self.X = None
self.Y = None
self.counter_array = np.zeros((1, 1))
self.gp = None
self.episode = 0
self.best_reward = np.empty((1, 1))
self.distance_penalty = 100
self.distance_penalty = 0
self.nr_policy_weights = nr_weights
self.nr_steps = nr_step
@ -40,6 +44,7 @@ class BayesianOptimization:
def initialize(self):
self.env.reset()
if self.env.render_mode == 'human':
self.env.render()
self.X = np.zeros((self.nr_init, self.nr_policy_weights))
@ -50,10 +55,11 @@ class BayesianOptimization:
self.X[i, :] = self.policy_model.weights.T
policy = self.policy_model.policy_rollout()
reward = self.runner(policy)
reward, step_count = self.runner(policy)
self.Y[i] = reward
self.gp = GaussianProcessRegressor(Matern(nu=1.5))
self.gp.fit(self.X, self.Y)
@ -66,6 +72,7 @@ class BayesianOptimization:
output = self.env.step(action)
env_reward += output[1]
done = output[2]
if self.env.render_mode == 'human':
time.sleep(0.0001)
step_count += 1
if step_count >= self.nr_steps:
@ -73,9 +80,21 @@ class BayesianOptimization:
distance = -(self.env.goal_position - output[0][0])
env_reward += distance * self.distance_penalty
if self.counter_array[0] == 0:
if step_count == 100:
step_count = np.NAN
self.counter_array[0] = step_count
else:
if step_count == 100:
step_count = np.NAN
self.counter_array = np.vstack((self.counter_array, step_count))
if self.env.render_mode == 'human':
time.sleep(0.25)
self.env.reset()
return env_reward
return env_reward, step_count
def next_observation(self):
if self.acq == 'ei':
@ -83,6 +102,31 @@ class BayesianOptimization:
self.X,
self.nr_test,
self.nr_policy_weights,
kappa=0,
seed=self.policy_seed,
lower=self.lowerb,
upper=self.upperb)
return x_next
elif self.acq == 'pi':
x_next = ProbabilityOfImprovement(self.gp,
self.X,
self.nr_test,
self.nr_policy_weights,
kappa=2.576,
seed=self.policy_seed,
lower=self.lowerb,
upper=self.upperb)
return x_next
elif self.acq == 'cb':
x_next = ConfidenceBound(self.gp,
self.X,
self.nr_test,
self.nr_policy_weights,
lam=2.576,
seed=self.policy_seed,
lower=self.lowerb,
upper=self.upperb)
@ -95,7 +139,7 @@ class BayesianOptimization:
def eval_new_observation(self, x_next):
self.policy_model.weights = x_next
policy = self.policy_model.policy_rollout()
reward = self.runner(policy)
reward, step_count = self.runner(policy)
self.X = np.vstack((self.X, x_next))
self.Y = np.vstack((self.Y, reward))
@ -108,28 +152,58 @@ class BayesianOptimization:
self.best_reward = np.vstack((self.best_reward, max(self.Y)))
self.episode += 1
self.policy_model.plot_policy()
return step_count
def plot_reward(self):
epsiodes = np.linspace(0, self.episode, self.episode)
plt.plot(epsiodes, self.best_reward)
plt.show()
def plot_objective_function(self):
plt.scatter(self.X[:, 0], self.Y)
x_dom = np.linspace(self.lowerb * np.ones((1, 6)), self.upperb * np.ones((1, 6)), 100).reshape(-1, self.nr_policy_weights)
y_plot, y_sigma = self.gp.predict(x_dom, return_std=True)
y_plot = y_plot.reshape(-1)
y_sigma = y_sigma.reshape(-1)
print(y_plot.shape, x_dom.shape)
plt.plot(x_dom[:, 0], y_plot)
plt.fill_between(
x_dom[:, 0],
y_plot - 1.96 * y_sigma,
y_plot + 1.96 * y_sigma,
alpha=0.5
)
plt.show()
def get_best_result(self):
y_hat = self.gp.predict(self.X)
idx = np.argmax(y_hat)
x_max = self.X[idx, :]
self.policy_model.weights = x_max
self.policy_model.policy_rollout()
print(self.counter_array[idx], idx)
self.policy_model.plot_policy(finished=self.counter_array[idx])
def main():
nr_steps = 100
env = Continuous_MountainCarEnv(render_mode='human')
bo = BayesianOptimization(env, nr_steps)
env = Continuous_MountainCarEnv() # render_mode='human'
bo = BayesianOptimization(env, nr_steps, acq='cb')
bo.initialize()
iteration_steps = 200
for i in range(iteration_steps):
x_next = bo.next_observation()
bo.eval_new_observation(x_next)
step_count = bo.eval_new_observation(x_next)
print(bo.episode, bo.best_reward[-1][0])
print(bo.episode, bo.best_reward[-1][0], step_count)
bo.plot_reward()
bo.policy_model.plot_policy()
bo.get_best_result()
plt.show()
if __name__ == "__main__":
main()

6
PolicyModel/GaussianModel.py
View File

@ -35,15 +35,15 @@ class GaussianPolicy:
return self.trajectory
def plot_policy(self):
def plot_policy(self, finished=np.NAN):
x = np.linspace(0, self.nr_steps, self.nr_steps)
plt.plot(x, self.trajectory)
if finished != np.NAN:
plt.vlines(finished, -1, 1, colors='red')
# for i in self.mean:
# gaussian = np.exp(-0.5 * (x - i)**2 / self.std**2)
# plt.plot(x, gaussian)
plt.show()
def main():
policy = GaussianPolicy(1, 50)