Fix werner-duvaud#89 (residual block)

games/atari.py

@@ -84,7 +84,7 @@ def __init__(self):
         self.batch_size = 1024  # Number of parts of games to train on at each training step
         self.checkpoint_interval = int(1e3)  # Number of training steps before using the model for self-playing
         self.value_loss_weight = 0.25  # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze)
-        self.train_on_gpu = True if torch.cuda.is_available() else False  # Train on GPU if available
+        self.train_on_gpu = torch.cuda.is_available()  # Train on GPU if available
 
         self.optimizer = "SGD"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization

games/breakout.py

@@ -84,7 +84,7 @@ def __init__(self):
         self.batch_size = 16  # Number of parts of games to train on at each training step
         self.checkpoint_interval = 500  # Number of training steps before using the model for self-playing
         self.value_loss_weight = 0.25  # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze)
-        self.train_on_gpu = True if torch.cuda.is_available() else False  # Train on GPU if available
+        self.train_on_gpu = torch.cuda.is_available()  # Train on GPU if available
 
         self.optimizer = "Adam"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization

games/cartpole.py

@@ -79,7 +79,7 @@ def __init__(self):
         self.batch_size = 128  # Number of parts of games to train on at each training step
         self.checkpoint_interval = 10  # Number of training steps before using the model for self-playing
         self.value_loss_weight = 1  # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze)
-        self.train_on_gpu = True if torch.cuda.is_available() else False  # Train on GPU if available
+        self.train_on_gpu = torch.cuda.is_available()  # Train on GPU if available
 
         self.optimizer = "Adam"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization

games/connect4.py

@@ -78,7 +78,7 @@ def __init__(self):
         self.batch_size = 64  # Number of parts of games to train on at each training step
         self.checkpoint_interval = 10  # Number of training steps before using the model for self-playing
         self.value_loss_weight = 0.25  # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze)
-        self.train_on_gpu = True if torch.cuda.is_available() else False  # Train on GPU if available
+        self.train_on_gpu = torch.cuda.is_available()  # Train on GPU if available
 
         self.optimizer = "Adam"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization

games/gomoku.py

@@ -79,7 +79,7 @@ def __init__(self):
         self.batch_size = 512  # Number of parts of games to train on at each training step
         self.checkpoint_interval = 50  # Number of training steps before using the model for self-playing
         self.value_loss_weight = 1  # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze)
-        self.train_on_gpu = True if torch.cuda.is_available() else False  # Train on GPU if available
+        self.train_on_gpu = torch.cuda.is_available()  # Train on GPU if available
 
         self.optimizer = "Adam"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization

games/gridworld.py

@@ -84,7 +84,7 @@ def __init__(self):
         self.batch_size = 128  # Number of parts of games to train on at each training step
         self.checkpoint_interval = 10  # Number of training steps before using the model for self-playing
         self.value_loss_weight = 1  # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze)
-        self.train_on_gpu = True if torch.cuda.is_available() else False  # Train on GPU if available
+        self.train_on_gpu = torch.cuda.is_available()  # Train on GPU if available
 
         self.optimizer = "Adam"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization

games/lunarlander.py

@@ -79,7 +79,7 @@ def __init__(self):
         self.batch_size = 64  # Number of parts of games to train on at each training step
         self.checkpoint_interval = 10  # Number of training steps before using the model for self-playing
         self.value_loss_weight = 1  # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze)
-        self.train_on_gpu = True if torch.cuda.is_available() else False  # Train on GPU if available
+        self.train_on_gpu = torch.cuda.is_available()  # Train on GPU if available
 
         self.optimizer = "Adam"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization

games/simple_grid.py

@@ -78,7 +78,7 @@ def __init__(self):
         self.batch_size = 32  # Number of parts of games to train on at each training step
         self.checkpoint_interval = 10  # Number of training steps before using the model for self-playing
         self.value_loss_weight = 1  # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze)
-        self.train_on_gpu = True if torch.cuda.is_available() else False  # Train on GPU if available
+        self.train_on_gpu = torch.cuda.is_available()  # Train on GPU if available
 
         self.optimizer = "Adam"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization

games/tictactoe.py

@@ -78,7 +78,7 @@ def __init__(self):
         self.batch_size = 64  # Number of parts of games to train on at each training step
         self.checkpoint_interval = 10  # Number of training steps before using the model for self-playing
         self.value_loss_weight = 0.25  # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze)
-        self.train_on_gpu = True if torch.cuda.is_available() else False  # Train on GPU if available
+        self.train_on_gpu = torch.cuda.is_available()  # Train on GPU if available
 
         self.optimizer = "Adam"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization

games/twentyone.py

@@ -85,7 +85,7 @@ def __init__(self):
         self.batch_size = 64  # Number of parts of games to train on at each training step
         self.checkpoint_interval = 10  # Number of training steps before using the model for self-playing
         self.value_loss_weight = 0.25  # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze)
-        self.train_on_gpu = True if torch.cuda.is_available() else False  # Train on GPU if available
+        self.train_on_gpu = torch.cuda.is_available()  # Train on GPU if available
 
         self.optimizer = "SGD"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization

models.py

@@ -219,14 +219,14 @@ def __init__(self, num_channels, stride=1):
         self.bn2 = torch.nn.BatchNorm2d(num_channels)
 
     def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = torch.nn.functional.relu(x)
-        x = self.conv2(x)
-        x = self.bn2(x)
-        x += x
-        x = torch.nn.functional.relu(x)
-        return x
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = torch.nn.functional.relu(out)
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out += x
+        out = torch.nn.functional.relu(out)
+        return out
 
 
 # Downsample observations before representation network (See paper appendix Network Architecture)