Create app.py

app.py

@@ -0,0 +1,29 @@
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+import seaborn as sns
+plt.style.use('seaborn-white')
+import pandas as pd
+from matplotlib import animation, rc
+import torch.nn.functional as F
+import torch
+import torch.nn as nn
+import torch.optim as optim
+plt.rcParams.update({'pdf.fonttype': 'truetype'})
+import pickle
+pc2 = pickle.load(open('price.pkl','rb'))
+def to_tensor(x):
+    return torch.from_numpy(np.array(x).astype(np.float32))
+def prediction(price_max,price_step,policy_net):
+    price_grid = np.arange(price_step, price_max, price_step)
+    sample_state = [0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., \
+                1.,     0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.]
+    Q_s = policy_net(to_tensor(sample_state))
+    a_opt = Q_s.max(0)[1].detach()
+    print(f'Optimal price action {price_grid[a_opt]}')
+    plt.figure(figsize=(16, 5))
+    plt.xlabel("Price action ($)")
+    plt.ylabel("Q ($)")
+    plt.bar(price_grid, Q_s.detach().numpy(), color='crimson',  width=6, alpha=0.8)
+    plt.show()
+prediction(500,10,pc2)