diff --git a/games/racecar.py b/games/racecar.py
index a78f96000261bb011a2b553e92c2e406775248fa..5ab30d5dbebc2210a65c86d945f9a88a916dd48c 100644
--- a/games/racecar.py
+++ b/games/racecar.py
@@ -30,9 +30,9 @@ class MuZeroConfig:
 
 
         ### Self-Play
-        self.num_workers = 10  # Number of simultaneous threads/workers self-playing to feed the replay buffer
+        self.num_workers = 50  # Number of simultaneous threads/workers self-playing to feed the replay buffer
         self.selfplay_on_gpu = False
-        self.max_moves = 2000  # Maximum number of moves if game is not finished before
+        self.max_moves = 1000  # Maximum number of moves if game is not finished before
         self.num_simulations = 50  # Number of future moves self-simulated
         self.discount = 0.999  # Chronological discount of the reward
         self.temperature_threshold = None  # Number of moves before dropping the temperature given by visit_softmax_temperature_fn to 0 (ie selecting the best action). If None, visit_softmax_temperature_fn is used every time
@@ -77,7 +77,7 @@ class MuZeroConfig:
         self.save_model = True  # Save the checkpoint in results_path as model.checkpoint
         self.training_steps = 100000  # Total number of training steps (ie weights update according to a batch)
         self.batch_size = 128  # Number of parts of games to train on at each training step
-        self.checkpoint_interval = 100  # Number of training steps before using the model for self-playing
+        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