diff --git a/model/pytorch/dcrnn_supervisor.py b/model/pytorch/dcrnn_supervisor.py
index a2d61c0..aa31eb0 100644
--- a/model/pytorch/dcrnn_supervisor.py
+++ b/model/pytorch/dcrnn_supervisor.py
@@ -119,6 +119,9 @@ class DCRNNSupervisor:
         wait = 0
         batches_seen = 0
         optimizer = torch.optim.Adam(self.dcrnn_model.parameters(), lr=base_lr)
+
+        lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=steps,
+                                                            gamma=lr_decay_ratio)
         criterion = torch.nn.L1Loss()  # mae loss
 
         self.dcrnn_model = self.dcrnn_model.train()
@@ -149,20 +152,23 @@ class DCRNNSupervisor:
 
                 optimizer.step()
 
+            lr_scheduler.step()
+
             val_loss = self.evaluate(dataset='val')
             end_time = time.time()
             if epoch_num % log_every == 0:
-                message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, val_mae: {:.4f} ' \
+                message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, val_mae: {:.4f}, lr: {:.6f}' \
                           '{:.1f}s'.format(epoch_num, epochs, batches_seen,
-                                           np.mean(losses), val_loss,
+                                           np.mean(losses), val_loss, lr_scheduler.get_lr(),
                                            (end_time - start_time))
                 self._logger.info(message)
 
             if epoch_num % test_every_n_epochs == 0:
                 test_loss = self.evaluate(dataset='test')
-                message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, test_mae: {:.4f} ' \
+                message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, test_mae: {:.4f},  lr: {:.6f} ' \
                           '{:.1f}s'.format(epoch_num, epochs, batches_seen,
-                                           np.mean(losses), test_loss, (end_time - start_time))
+                                           np.mean(losses), test_loss, lr_scheduler.get_lr(),
+                                           (end_time - start_time))
                 self._logger.info(message)
 
             if val_loss < min_val_loss:
@@ -171,8 +177,8 @@ class DCRNNSupervisor:
                 if save_model:
                     model_file_name = self.save_model(epoch_num)
                     self._logger.info(
-                        'Val loss decrease from {:.4f} to {:.4f}, saving to {}'.format(min_val_loss, val_loss,
-                                                                                       model_file_name))
+                        'Val loss decrease from {:.4f} to {:.4f}, '
+                        'saving to {}'.format(min_val_loss, val_loss, model_file_name))
             elif val_loss >= min_val_loss:
                 wait += 1
                 if wait == patience: