noise.py

import numpy as np
import matplotlib.pyplot as plt

class OrnsteinUhlenbeckActionNoise:
    def __init__(self, mu, sigma=0.2, theta=0.15, dt=1e-2, x0=None):
        self.theta = theta
        self.mu = mu
        self.sigma = sigma
        self.dt = dt
        self.x0 = x0
        self.reset()
 
    def __call__(self):
        x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + \
            self.sigma * np.sqrt(self.dt) * np.random.normal(size=self.mu.shape)
        self.x_prev = x
        return x
 
    def reset(self):
        self.x_prev = self.x0 if self.x0 is not None else np.zeros_like(self.mu)
 
    def __repr__(self):
        return 'OrnsteinUhlenbeckActionNoise(mu={}, sigma={})'.format(self.mu, self.sigma)


def main():
    # ou_noise=OrnsteinUhlenbeckActionNoise(mu=np.zeros(2), sigma=0.5)
    start = 0.25*np.random.normal(size=(2,))
    ou_noise=OrnsteinUhlenbeckActionNoise(mu=start, sigma=0.25, x0=start)
    plt.figure('data')
    y=[]
    t=np.linspace(0,100,1000)
    for _ in t:
        y.append(ou_noise())
    plt.plot(t,y)
    plt.show()