Made sure everything went to their appropriate devices

examples/acrobot.py

@@ -90,11 +90,11 @@ logwriter = rltorch.log.LogWriter(logger, SummaryWriter())
 # Setting up the networks
 device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
 net = rn.Network(Value(state_size, action_size), 
-                    torch.optim.Adam, config, logger = logger, name = "DQN")
-target_net = rn.TargetNetwork(net)
+                    torch.optim.Adam, config, device = device, logger = logger, name = "DQN")
+target_net = rn.TargetNetwork(net, device = device)
 
 # Actor takes a net and uses it to produce actions from given states
-actor = ArgMaxSelector(net, action_size)
+actor = ArgMaxSelector(net, action_size, device = device)
 # Memory stores experiences for later training
 memory = M.ReplayMemory(capacity = config['memory_size'])
 

examples/pong.py

@@ -107,11 +107,11 @@ logwriter = rltorch.log.LogWriter(logger, SummaryWriter())
 # Setting up the networks
 device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
 net = rn.Network(Value(state_size, action_size), 
-                    torch.optim.Adam, config, logger = logger, name = "DQN")
-target_net = rn.TargetNetwork(net)
+                    torch.optim.Adam, config, device = device, logger = logger, name = "DQN")
+target_net = rn.TargetNetwork(net, device = device)
 
 # Actor takes a network and uses it to produce actions from given states
-actor = ArgMaxSelector(net, action_size)
+actor = ArgMaxSelector(net, action_size, device = device)
 # Memory stores experiences for later training
 memory = M.ReplayMemory(capacity = config['memory_size'])
 

rltorch/action_selector/ArgMaxSelector.py

@@ -10,7 +10,7 @@ class ArgMaxSelector:
     def best_act(self, state):
         with torch.no_grad():
             if self.device is not None:
-                self.device.to(self.device)
+                state = state.to(self.device)
             action_values = self.model(state).squeeze(0)
             action = self.random_act() if (action_values[0] == action_values).all() else action_values.argmax().item()
         return action

rltorch/network/Network.py

@@ -2,11 +2,14 @@ class Network:
     """
     Wrapper around model which provides copy of it instead of trained weights
     """
-    def __init__(self, model, optimizer, config, logger = None, name = ""):
+    def __init__(self, model, optimizer, config, device = None, logger = None, name = ""):
         self.model = model
         self.optimizer = optimizer(model.parameters(), lr = config['learning_rate'], weight_decay = config['weight_decay'])
         self.logger = logger
         self.name = name
+        self.device = device
+        if self.device is not None:
+            self.model = self.model.to(device)
 
     def __call__(self, *args):
         return self.model(*args)

rltorch/network/TargetNetwork.py

@@ -4,9 +4,11 @@ class TargetNetwork:
     """
     Wrapper around model which provides copy of it instead of trained weights
     """
-    def __init__(self, network):
+    def __init__(self, network, device = None):
         self.model = network.model
         self.target_model = deepcopy(network.model)
+        if network.device is not None:
+            self.target_model = self.target_model.to(network.device)
 
     def __call__(self, *args):
         return self.model(*args)