diff --git a/.gitmodules b/.gitmodules
index 0491e57c..080163a6 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,3 +1,6 @@
 [submodule "simzoo/envs/oscillator"]
 	path = simzoo/envs/oscillator
-	url = git@github.com:rickstaa/oscillator.git
+	url = ../oscillator.git
+[submodule "simzoo/envs/ex3_ekf"]
+	path = simzoo/envs/ex3_ekf
+	url = ../ex3_ekf.git
diff --git a/simzoo/envs/Ex3_EKF/Ex3_EKF copy.py b/simzoo/envs/Ex3_EKF/Ex3_EKF copy.py
deleted file mode 100644
index 685e4d8d..00000000
--- a/simzoo/envs/Ex3_EKF/Ex3_EKF copy.py	
+++ /dev/null
@@ -1,187 +0,0 @@
-import math
-import gym
-from gym import spaces, logger
-from gym.utils import seeding
-import numpy as np
-import csv
-import matplotlib.pyplot as plt
-
-# This example is the RL based stationary Kalman filter
-
-# the dynamic system whose state is to be estimated:
-# x(k+1)=Ax(k)+w(k)
-# x_1: angle
-# x_2: frequency
-# x_3: amplitude
-# y(k)=x_3(k)*sin(x_1(k))+v(k)
-# A=[1,dt,0;0,1,0;0,0,1]
-# x(0)~N([0;10;1],[3,0,0;0,3,0;0,0,3])
-# w(k)~N([0;0;0],[1/3*(dt)^3*q_1,1/2*(dt)^2*q_1,0;1/2*(dt)^2*q_1,dt*q_1,0;0,0,dt*q_2])
-# v(k)~N(0,1)
-
-# estimator design
-# \hat(x)(k+1)=A\hat(x)(k)+u
-# where u=[u1,u2,u3]', u=l(\hat(x)(k),y(k)) come from the policy network l(.,.)
-
-
-class Ex3_EKF(gym.Env):
-    def __init__(self):
-
-        self.t = 0
-        self.dt = 0.1
-        self.q1 = 0.01
-        self.g = 9.81
-        # self.l = max(0.5,1 + np.random.normal(0,0.5))
-        self.l = 1
-        # self.mean0 = [1.5, 0]
-        # self.cov0_1 = 0.1
-        # self.cov0_2 = 0.1
-        # self.cov0_1 = 0
-        # self.cov0_2 = 0
-        self.mean1 = [0, 0]
-        self.cov1 = np.array(
-            [
-                [1 / 3 * (self.dt) ** 3 * self.q1, 1 / 2 * (self.dt) ** 2 * self.q1],
-                [1 / 2 * (self.dt) ** 2 * self.q1, self.dt * self.q1],
-            ]
-        )
-        # self.cov1 = np.array([[0,0],[0,0]])
-        self.mean2 = 0
-        self.cov2 = 1e-2
-        # self.cov2 = 0
-        self.missing_rate = 0
-        self.sigma = 0
-        # displacement limit set to be [-high, high]
-        high = np.array([10000, 10000])
-
-        self.action_space = spaces.Box(
-            low=np.array([-10.0, -10.0]), high=np.array([10.0, 10.0]), dtype=np.float32
-        )
-        self.observation_space = spaces.Box(-high, high, dtype=np.float32)
-
-        self.seed()
-        self.viewer = None
-        self.state = None
-        self.output = None
-
-        self.steps_beyond_done = None
-
-    def seed(self, seed=None):
-        self.np_random, seed = seeding.np_random(seed)
-        return [seed]
-
-    def step(self, action):
-        u1, u2 = action
-        t = self.t
-        input = 0 * np.cos(t) * self.dt
-        # Slave
-        hat_x_1, hat_x_2, x_1, x_2 = self.state
-        # y_1 = self.output
-        # hat_y_1 = np.sin(hat_x_1)
-        #
-        # hat_x_1 = hat_x_1 + self.dt * hat_x_2 + self.dt * u1*(y_1-hat_y_1)
-        # hat_x_2 = hat_x_2 - self.g*np.sin(hat_x_1)*self.dt + self.dt * u2*(y_1-hat_y_1) + input
-
-        # Master
-
-        x_1 = x_1 + self.dt * x_2
-        x_2 = x_2 - self.g * self.l * np.sin(x_1) * self.dt + input
-
-        state = np.array([x_1, x_2])
-        # add process noise
-        state = state + np.random.multivariate_normal(self.mean1, self.cov1).flatten()
-        x_1, x_2 = state
-
-        y_1 = np.sin(x_1) + np.random.normal(self.mean2, np.sqrt(self.cov2))
-        hat_y_1 = np.sin(hat_x_1 + self.dt * hat_x_2)
-
-        # flag=1: received
-        # flag=0: dropout
-        (flag,) = np.random.binomial(1, 1 - self.missing_rate, 1)
-        # drop_rate = 1
-        # to construct cost
-        if flag == 1:
-            hat_x_1 = hat_x_1 + self.dt * hat_x_2 + self.dt * u1 * (y_1 - hat_y_1)
-            hat_x_2 = (
-                hat_x_2
-                - self.g * np.sin(hat_x_1) * self.dt
-                + self.dt * u2 * (y_1 - hat_y_1)
-                + input
-            )
-        else:
-            hat_x_1 = hat_x_1 + self.dt * hat_x_2
-            hat_x_2 = hat_x_2 - self.g * np.sin(hat_x_1) * self.dt + input
-
-        # hat_x_1 = hat_x_1 + self.dt * hat_x_2 + self.dt * u1 * (y_1 - hat_y_1)
-        # hat_x_2 = hat_x_2 - self.g * np.sin(hat_x_1) * self.dt + self.dt * u2 * (y_1 - hat_y_1) + input
-
-        cost_u = (
-            np.square(u1 * (y_1 - hat_y_1)) * self.dt
-            + np.square(u2 * (y_1 - hat_y_1)) * self.dt
-        )
-        # cost_y = np.abs(hat_y_1 - y_1) * self.dt
-        # cost = cost_y
-        cost = np.square(hat_x_1 - x_1) + np.square(hat_x_2 - x_2)
-        # cost = np.abs(hat_x_1 - x_1)**1 + np.abs(hat_x_2 - x_2)**1
-        # print('cost',cost)
-        if cost > 100:
-            done = True
-        else:
-            done = False
-
-        # update new for next round
-        self.state = np.array([hat_x_1, hat_x_2, x_1, x_2])
-        self.output = y_1
-        self.t = self.t + self.dt
-
-        # return np.array([hat_x_1,hat_x_2,y_1, y_2]), cost, done, dict(reference=y_1, state_of_interest=np.array([hat_y_1,hat_y_2]))
-
-        return (
-            np.array([hat_x_1, hat_x_2]),
-            cost,
-            done,
-            dict(
-                reference=y_1,
-                state_of_interest=np.array([hat_x_1 - x_1, hat_x_2 - x_2]),
-            ),
-        )
-
-    def reset(self):
-        x_1 = np.random.uniform(-np.pi / 2, np.pi / 2)
-        x_2 = np.random.uniform(-np.pi / 2, np.pi / 2)
-        hat_x_1 = x_1 + np.random.uniform(-np.pi / 4, np.pi / 4)
-        hat_x_2 = x_2 + np.random.uniform(-np.pi / 4, np.pi / 4)
-        self.state = np.array([hat_x_1, hat_x_2, x_1, x_2])
-        self.output = np.sin(x_1) + np.random.normal(self.mean2, np.sqrt(self.cov2))
-        y_1 = self.output
-        y_2 = np.sin(x_2) + np.random.normal(self.mean2, np.sqrt(self.cov2))
-        return np.array([hat_x_1, hat_x_2])
-
-    def render(self, mode="human"):
-
-        return
-
-
-if __name__ == "__main__":
-    env = Ex3_EKF()
-    T = 10
-    path = []
-    t1 = []
-    s = env.reset()
-    for i in range(int(T / env.dt)):
-        s, r, info, done = env.step(np.array([0, 0]))
-        path.append(s)
-        t1.append(i * env.dt)
-
-    fig = plt.figure(figsize=(9, 6))
-    ax = fig.add_subplot(111)
-
-    ax.plot(t1, np.array(path)[:, 0], color="yellow", label="x1")
-    ax.plot(t1, np.array(path)[:, 1], color="green", label="x2")
-    # ax.plot(t1, np.array(path)[:, 2], color='black', label='measurement')
-
-    handles, labels = ax.get_legend_handles_labels()
-    #
-    ax.legend(handles, labels, loc=2, fancybox=False, shadow=False)
-    plt.show()
-    print("done")
diff --git a/simzoo/envs/Ex3_EKF/Ex3_EKF.py b/simzoo/envs/Ex3_EKF/Ex3_EKF.py
deleted file mode 100644
index 685e4d8d..00000000
--- a/simzoo/envs/Ex3_EKF/Ex3_EKF.py
+++ /dev/null
@@ -1,187 +0,0 @@
-import math
-import gym
-from gym import spaces, logger
-from gym.utils import seeding
-import numpy as np
-import csv
-import matplotlib.pyplot as plt
-
-# This example is the RL based stationary Kalman filter
-
-# the dynamic system whose state is to be estimated:
-# x(k+1)=Ax(k)+w(k)
-# x_1: angle
-# x_2: frequency
-# x_3: amplitude
-# y(k)=x_3(k)*sin(x_1(k))+v(k)
-# A=[1,dt,0;0,1,0;0,0,1]
-# x(0)~N([0;10;1],[3,0,0;0,3,0;0,0,3])
-# w(k)~N([0;0;0],[1/3*(dt)^3*q_1,1/2*(dt)^2*q_1,0;1/2*(dt)^2*q_1,dt*q_1,0;0,0,dt*q_2])
-# v(k)~N(0,1)
-
-# estimator design
-# \hat(x)(k+1)=A\hat(x)(k)+u
-# where u=[u1,u2,u3]', u=l(\hat(x)(k),y(k)) come from the policy network l(.,.)
-
-
-class Ex3_EKF(gym.Env):
-    def __init__(self):
-
-        self.t = 0
-        self.dt = 0.1
-        self.q1 = 0.01
-        self.g = 9.81
-        # self.l = max(0.5,1 + np.random.normal(0,0.5))
-        self.l = 1
-        # self.mean0 = [1.5, 0]
-        # self.cov0_1 = 0.1
-        # self.cov0_2 = 0.1
-        # self.cov0_1 = 0
-        # self.cov0_2 = 0
-        self.mean1 = [0, 0]
-        self.cov1 = np.array(
-            [
-                [1 / 3 * (self.dt) ** 3 * self.q1, 1 / 2 * (self.dt) ** 2 * self.q1],
-                [1 / 2 * (self.dt) ** 2 * self.q1, self.dt * self.q1],
-            ]
-        )
-        # self.cov1 = np.array([[0,0],[0,0]])
-        self.mean2 = 0
-        self.cov2 = 1e-2
-        # self.cov2 = 0
-        self.missing_rate = 0
-        self.sigma = 0
-        # displacement limit set to be [-high, high]
-        high = np.array([10000, 10000])
-
-        self.action_space = spaces.Box(
-            low=np.array([-10.0, -10.0]), high=np.array([10.0, 10.0]), dtype=np.float32
-        )
-        self.observation_space = spaces.Box(-high, high, dtype=np.float32)
-
-        self.seed()
-        self.viewer = None
-        self.state = None
-        self.output = None
-
-        self.steps_beyond_done = None
-
-    def seed(self, seed=None):
-        self.np_random, seed = seeding.np_random(seed)
-        return [seed]
-
-    def step(self, action):
-        u1, u2 = action
-        t = self.t
-        input = 0 * np.cos(t) * self.dt
-        # Slave
-        hat_x_1, hat_x_2, x_1, x_2 = self.state
-        # y_1 = self.output
-        # hat_y_1 = np.sin(hat_x_1)
-        #
-        # hat_x_1 = hat_x_1 + self.dt * hat_x_2 + self.dt * u1*(y_1-hat_y_1)
-        # hat_x_2 = hat_x_2 - self.g*np.sin(hat_x_1)*self.dt + self.dt * u2*(y_1-hat_y_1) + input
-
-        # Master
-
-        x_1 = x_1 + self.dt * x_2
-        x_2 = x_2 - self.g * self.l * np.sin(x_1) * self.dt + input
-
-        state = np.array([x_1, x_2])
-        # add process noise
-        state = state + np.random.multivariate_normal(self.mean1, self.cov1).flatten()
-        x_1, x_2 = state
-
-        y_1 = np.sin(x_1) + np.random.normal(self.mean2, np.sqrt(self.cov2))
-        hat_y_1 = np.sin(hat_x_1 + self.dt * hat_x_2)
-
-        # flag=1: received
-        # flag=0: dropout
-        (flag,) = np.random.binomial(1, 1 - self.missing_rate, 1)
-        # drop_rate = 1
-        # to construct cost
-        if flag == 1:
-            hat_x_1 = hat_x_1 + self.dt * hat_x_2 + self.dt * u1 * (y_1 - hat_y_1)
-            hat_x_2 = (
-                hat_x_2
-                - self.g * np.sin(hat_x_1) * self.dt
-                + self.dt * u2 * (y_1 - hat_y_1)
-                + input
-            )
-        else:
-            hat_x_1 = hat_x_1 + self.dt * hat_x_2
-            hat_x_2 = hat_x_2 - self.g * np.sin(hat_x_1) * self.dt + input
-
-        # hat_x_1 = hat_x_1 + self.dt * hat_x_2 + self.dt * u1 * (y_1 - hat_y_1)
-        # hat_x_2 = hat_x_2 - self.g * np.sin(hat_x_1) * self.dt + self.dt * u2 * (y_1 - hat_y_1) + input
-
-        cost_u = (
-            np.square(u1 * (y_1 - hat_y_1)) * self.dt
-            + np.square(u2 * (y_1 - hat_y_1)) * self.dt
-        )
-        # cost_y = np.abs(hat_y_1 - y_1) * self.dt
-        # cost = cost_y
-        cost = np.square(hat_x_1 - x_1) + np.square(hat_x_2 - x_2)
-        # cost = np.abs(hat_x_1 - x_1)**1 + np.abs(hat_x_2 - x_2)**1
-        # print('cost',cost)
-        if cost > 100:
-            done = True
-        else:
-            done = False
-
-        # update new for next round
-        self.state = np.array([hat_x_1, hat_x_2, x_1, x_2])
-        self.output = y_1
-        self.t = self.t + self.dt
-
-        # return np.array([hat_x_1,hat_x_2,y_1, y_2]), cost, done, dict(reference=y_1, state_of_interest=np.array([hat_y_1,hat_y_2]))
-
-        return (
-            np.array([hat_x_1, hat_x_2]),
-            cost,
-            done,
-            dict(
-                reference=y_1,
-                state_of_interest=np.array([hat_x_1 - x_1, hat_x_2 - x_2]),
-            ),
-        )
-
-    def reset(self):
-        x_1 = np.random.uniform(-np.pi / 2, np.pi / 2)
-        x_2 = np.random.uniform(-np.pi / 2, np.pi / 2)
-        hat_x_1 = x_1 + np.random.uniform(-np.pi / 4, np.pi / 4)
-        hat_x_2 = x_2 + np.random.uniform(-np.pi / 4, np.pi / 4)
-        self.state = np.array([hat_x_1, hat_x_2, x_1, x_2])
-        self.output = np.sin(x_1) + np.random.normal(self.mean2, np.sqrt(self.cov2))
-        y_1 = self.output
-        y_2 = np.sin(x_2) + np.random.normal(self.mean2, np.sqrt(self.cov2))
-        return np.array([hat_x_1, hat_x_2])
-
-    def render(self, mode="human"):
-
-        return
-
-
-if __name__ == "__main__":
-    env = Ex3_EKF()
-    T = 10
-    path = []
-    t1 = []
-    s = env.reset()
-    for i in range(int(T / env.dt)):
-        s, r, info, done = env.step(np.array([0, 0]))
-        path.append(s)
-        t1.append(i * env.dt)
-
-    fig = plt.figure(figsize=(9, 6))
-    ax = fig.add_subplot(111)
-
-    ax.plot(t1, np.array(path)[:, 0], color="yellow", label="x1")
-    ax.plot(t1, np.array(path)[:, 1], color="green", label="x2")
-    # ax.plot(t1, np.array(path)[:, 2], color='black', label='measurement')
-
-    handles, labels = ax.get_legend_handles_labels()
-    #
-    ax.legend(handles, labels, loc=2, fancybox=False, shadow=False)
-    plt.show()
-    print("done")
diff --git a/simzoo/envs/Ex3_EKF/__init__.py b/simzoo/envs/Ex3_EKF/__init__.py
deleted file mode 100644
index ead6e06f..00000000
--- a/simzoo/envs/Ex3_EKF/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import sys
-
-# Check if used as name_space package
-if "simzoo" in sys.modules:
-    from simzoo.envs.Ex3_EKF.Ex3_EKF import Ex3_EKF
-else:
-    from machine_learning_control.simzoo.simzoo.envs.Ex3_EKF.Ex3_EKF import Ex3_EKF
diff --git a/simzoo/envs/ex3_ekf b/simzoo/envs/ex3_ekf
new file mode 160000
index 00000000..caa7f081
--- /dev/null
+++ b/simzoo/envs/ex3_ekf
@@ -0,0 +1 @@
+Subproject commit caa7f081f8a2db016c4267629c61f1f1efb369ad
diff --git a/simzoo/envs/oscillator b/simzoo/envs/oscillator
index 806a859b..a5c16790 160000
--- a/simzoo/envs/oscillator
+++ b/simzoo/envs/oscillator
@@ -1 +1 @@
-Subproject commit 806a859b3ab306224fe4e0a236583c76f4012d4e
+Subproject commit a5c1679093e4a77280564b7f4efb70b88c8d2efb