Update 2d-meta_train.py

Change some parameters for performance

Author: jieyibi

examples/2d-meta_train.py

@@ -35,7 +35,7 @@ def main():
 
     # Example callbacks
     checkpoint_callback = ModelCheckpoint(
-        dirpath="checkpoints",  # save to checkpoints/
+        dirpath="meta_pomo/checkpoints",  # save to checkpoints/
         filename="epoch_{epoch:03d}",  # save as epoch_XXX.ckpt
         save_top_k=1,  # save only the best model
         save_last=True,  # save the last model
@@ -47,8 +47,8 @@ def main():
     # Meta callbacks
     meta_callback = ReptileCallback(
         num_tasks = 1,  # the number of tasks in a mini-batch, i.e. `B` in the original paper
-        alpha = 0.99,  # initial weight of the task model for the outer-loop optimization of reptile
-        alpha_decay = 0.999,  # weight decay of the task model for the outer-loop optimization of reptile
+        alpha = 0.9,  # initial weight of the task model for the outer-loop optimization of reptile
+        alpha_decay = 1,  # weight decay of the task model for the outer-loop optimization of reptile. No decay performs better.
         min_size = 20,  # minimum of sampled size in meta tasks (only supported in cross-size generalization)
         max_size= 150,  # maximum of sampled size in meta tasks (only supported in cross-size generalization)
         data_type="size_distribution",  # choose from ["size", "distribution", "size_distribution"]
@@ -63,7 +63,7 @@ def main():
 
     # Adjust your trainer to the number of epochs you want to run
     trainer = RL4COTrainer(
-        max_epochs=20000,  # (the number of meta_model updates) * (the number of tasks in a mini-batch)
+        max_epochs=15000,  # (the number of meta_model updates) * (the number of tasks in a mini-batch)
         callbacks=callbacks,
         accelerator="gpu",
         devices=[device_id],