requirements

main.py

@@ -5,26 +5,27 @@ import gym
 import numpy as np
 import pandas as pd
 from scipy.stats import gaussian_kde as gkde
+from scipy.stats import norm
 
 
 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]
+    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):
+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
+    score = 0
     for i in range(10000):
         action = 1 if decision.T @ observation < 0 else 0
         observation, reward, done, info = env.step(action)
@@ -34,14 +35,14 @@ def test(decision=np.array([-0.09, -0.71, -0.43 , -0.74]), seed=1992):
             if score == 500:
                 print("WIN")
             else:
-                print("LOSE: {int(score)}")
-            score = 1  # reset score
+                print(f"LOSE: {int(score)}")
+            score = 0  # reset score
             observation, info = env.reset(return_info=True)
     env.close()
 
 
 if __name__ == "__main__":
-    data = pickle.load(open('data.pkl','rb'))
+    data = pickle.load(open("data.pkl", "rb"))
     mud_point = train(data)
-    print("MUD Point: {mud_point}")
+    print(f"MUD Point: {mud_point}")
     test(mud_point)

requirements.txt

@@ -0,0 +1,5 @@
+scipy
+numpy
+gym[classic_control]
+matplotlib
+pandas