kleine Anpassungen

dqn_model.py

@@ -2,6 +2,7 @@
 import torch.nn as nn
 import torch.optim as optim
 
+
 class DQN(nn.Module):
     def __init__(self, inp_size: int, hidden_size, out_size: int, lr, gamma, device):
         super(DQN, self).__init__()
@@ -24,20 +25,22 @@ def __init__(self, inp_size: int, hidden_size, out_size: int, lr, gamma, device)
         self.flatten = nn.Flatten()
         self.run_device = device
 
-
     def forward(self, x):
-        if len(x.shape)==1:
-            x=x.unsqueeze(0)
+        if len(x.shape) == 1:
+            x = x.unsqueeze(0)
         x = self.flatten(x)
         return self.rls(x)
 
     def save(self, name):
         torch.save(self.state_dict(), name)
 
     def train_step(self, old_state, new_state, action, reward):
+        # Da wir uns nicht sicher waren, ob unsere ursprüngliche Implementierung funktioniert hat, haben wir uns
+        # an diesem Code von dem Spiel Snake orientiert und ihn für uns angepasst:
+        # https://github.com/patrickloeber/snake-ai-pytorch/blob/main/model.py
         old_state = torch.from_numpy(old_state).to(self.run_device)
         new_state = torch.from_numpy(new_state).to(self.run_device)
-        action = torch.tensor(action, dtype = torch.float).to(self.run_device)
+        action = torch.tensor(action, dtype=torch.float).to(self.run_device)
         reward = torch.tensor(reward, dtype=torch.float).to(self.run_device)
         if len(old_state.shape) == 1:
             old_state = torch.unsqueeze(old_state, 0)