Optimization of some parameters for tictactoe.

games/tictactoe.py

@@ -75,18 +75,18 @@ def __init__(self):
         self.results_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "../results", os.path.basename(__file__)[:-3], datetime.datetime.now().strftime("%Y-%m-%d--%H-%M-%S"))  # Path to store the model weights and TensorBoard logs
         self.save_model = True  # Save the checkpoint in results_path as model.checkpoint
         self.training_steps = 1000000  # Total number of training steps (ie weights update according to a batch)
-        self.batch_size = 64  # Number of parts of games to train on at each training step
+        self.batch_size = 1024  # 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 = torch.cuda.is_available()  # Train on GPU if available
 
-        self.optimizer = "Adam"  # "Adam" or "SGD". Paper uses SGD
+        self.optimizer = "SGD"  # "Adam" or "SGD". Paper uses SGD
         self.weight_decay = 1e-4  # L2 weights regularization
         self.momentum = 0.9  # Used only if optimizer is SGD
 
         # Exponential learning rate schedule
-        self.lr_init = 0.003  # Initial learning rate
-        self.lr_decay_rate = 1  # Set it to 1 to use a constant learning rate
+        self.lr_init = 0.3  # Initial learning rate
+        self.lr_decay_rate = 0.97  # Set it to 1 to use a constant learning rate
         self.lr_decay_steps = 10000