diff --git a/main.py b/main.py
new file mode 100644
index 0000000..88a0a81
--- /dev/null
+++ b/main.py
@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+import pickle
+
+import gym
+import numpy as np
+import pandas as pd
+from scipy.stats import gaussian_kde as gkde
+
+
+def train(data):
+    D = pd.DataFrame(data)
+    D['qoi'] = D['obs'].apply(lambda o: np.sum(o,axis=0)/np.sqrt(len(o)))
+    D['i'] = D['lam'].apply(lambda l: norm.pdf(l).prod())
+    D['o'] = D['qoi'].apply(lambda q: norm.pdf(q).prod())
+    Q = np.array(D['qoi'].to_list()).reshape(-1,4)
+    K = [gkde(Q[:,i]) for i in range(4)]
+    D['p'] = D['qoi'].apply(lambda q: np.prod([ K[i].pdf(q[i]) for i in range(4)]))
+    D['u'] = D['i']*D['o']/D['p']
+    mud_point_idx = D['u'].argmax()
+    mud_point = D['lam'].iloc[mud_point_idx]
+    return mud_point
+
+
+def test(decision=np.array([-0.09, -0.71, -0.43 , -0.74]), seed=1992):
+    env = gym.make("CartPole-v1")
+    observation, info = env.reset(seed=seed, return_info=True)
+    score = 1
+    for i in range(10000):
+        action = 1 if decision.T @ observation < 0 else 0
+        observation, reward, done, info = env.step(action)
+        score += reward
+        env.render()
+        if done:
+            if score == 500:
+                print("WIN")
+            else:
+                print("LOSE: {int(score)}")
+            score = 1  # reset score
+            observation, info = env.reset(return_info=True)
+    env.close()
+
+
+if __name__ == "__main__":
+    data = pickle.load(open('data.pkl','rb'))
+    mud_point = train(data)
+    print("MUD Point: {mud_point}")
+    test(mud_point)