diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..ab0f6c8
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+*.pt
+*.pyc
\ No newline at end of file
diff --git a/MDCVRP/VRP_Actor.py b/MDCVRP/VRP_Actor.py
index eab75a8..1645ff9 100644
--- a/MDCVRP/VRP_Actor.py
+++ b/MDCVRP/VRP_Actor.py
@@ -209,7 +209,7 @@ def __init__(self, input_dim, hidden_dim):
 
     def forward(self, encoder_inputs, pool,capcity,demand, n_steps,num_depots,T, greedy=False):
         num_depot = num_depots
-        print(num_depot)
+        #print(num_depot)
         mask1 = encoder_inputs.new_zeros((encoder_inputs.size(0), encoder_inputs.size(1)))
         mask = encoder_inputs.new_zeros((encoder_inputs.size(0), encoder_inputs.size(1)))
 
diff --git a/MDCVRP/VRP_Rollout_train.py b/MDCVRP/VRP_Rollout_train.py
index d7735a1..07e550d 100644
--- a/MDCVRP/VRP_Rollout_train.py
+++ b/MDCVRP/VRP_Rollout_train.py
@@ -16,6 +16,7 @@
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 #device = torch.device('cpu')
+num_depots=2
 n_nodes = 22#nodes = nodes + depots
 steps = n_nodes
 def rollout(model, dataset,batch_size, n_nodes):
@@ -91,7 +92,7 @@ def get_runs(params):
             scheduler = LambdaLR(actor_optim, lr_lambda=lambda f: 0.96 ** epoch)
             for batch_idx, batch in enumerate(data_loder):
                 batch = batch.to(device)
-                tour_indices, tour_logp = actor(batch,steps*2)
+                tour_indices, tour_logp = actor(batch,steps*2,num_depots)
 
                 rewar = reward1(batch.x, tour_indices.detach(),n_nodes)
                 base_reward = rol_baseline.eval(batch,steps)
diff --git a/MDCVRP/creat_vrp.py b/MDCVRP/creat_vrp.py
index e5b4122..fed2480 100644
--- a/MDCVRP/creat_vrp.py
+++ b/MDCVRP/creat_vrp.py
@@ -148,8 +148,8 @@ def c_dist(x1,x2):
 def reward1(static, tour_indices,n_nodes):
 
     static = static.reshape(-1,n_nodes,2)
-    print(static.shape)
-    static = torch.from_numpy(static).to('cuda')
+    #print(static.shape)
+    static = static.to('cuda')
     static = static.transpose(2,1)
 
     tour_indices_1 = deepcopy(tour_indices)