diff --git a/config.yaml b/config.yaml
index 2bf65d85..029b1bb1 100755
--- a/config.yaml
+++ b/config.yaml
@@ -6,7 +6,8 @@ env_args:
     #env: 'handyrl.envs.parallel_tictactoe'  # specify by path
 
 train_args:
-    turn_based_training: True
+    turn_based_training: False  # for turn-based games
+    zero_sum_averaging: False  # for 2p zero-sum games
     observation: False
     gamma: 0.8
     forward_steps: 16
diff --git a/handyrl/train.py b/handyrl/train.py
index 07dbf81f..594fe3bf 100755
--- a/handyrl/train.py
+++ b/handyrl/train.py
@@ -53,7 +53,7 @@ def replace_none(a, b):
         moments_ = sum([pickle.loads(bz2.decompress(ms)) for ms in ep['moment']], [])
         moments = moments_[ep['start'] - ep['base']:ep['end'] - ep['base']]
         players = list(moments[0]['observation'].keys())
-        if not args['turn_based_training']:  # solo training
+        if not (args['turn_based_training'] or args['zero_sum_averaging']):  # solo training
             players = [random.choice(players)]
 
         obs_zeros = map_r(moments[0]['observation'][moments[0]['turn'][0]], lambda o: np.zeros_like(o))  # template for padding
@@ -236,7 +236,8 @@ def compute_loss(batch, model, hidden, args):
 
     if 'value' in outputs_nograd:
         values_nograd = outputs_nograd['value']
-        if args['turn_based_training'] and values_nograd.size(2) == 2:  # two player zerosum game
+        if args['zero_sum_averaging']:  # two player zerosum game
+            assert values_nograd.size(2) == 2
             values_nograd_opponent = -torch.stack([values_nograd[:, :, 1], values_nograd[:, :, 0]], dim=2)
             values_nograd = (values_nograd + values_nograd_opponent) / (batch['observation_mask'].sum(dim=2, keepdim=True) + 1e-8)
         outputs_nograd['value'] = values_nograd * emasks + batch['outcome'] * (1 - emasks)