diff --git a/AlphaGo/ai.py b/AlphaGo/ai.py
index 437759960..0c2157c8b 100644
--- a/AlphaGo/ai.py
+++ b/AlphaGo/ai.py
@@ -87,7 +87,7 @@ def get_moves(self, states):
 class MCTSPlayer(object):
     def __init__(self, value_function, policy_function, rollout_function, lmbda=.5, c_puct=5, rollout_limit=500, playout_depth=40, n_playout=100):
         self.mcts = mcts.MCTS(value_function, policy_function, rollout_function, lmbda, c_puct,
-                    rollout_limit, playout_depth, n_playout)
+                              rollout_limit, playout_depth, n_playout)
 
     def get_move(self, state):
         sensible_moves = [move for move in state.get_legal_moves(include_eyes=False)]
diff --git a/AlphaGo/go.py b/AlphaGo/go.py
index 48b696fda..f56df4c16 100644
--- a/AlphaGo/go.py
+++ b/AlphaGo/go.py
@@ -427,7 +427,7 @@ def do_move(self, action, color=None):
         # Check for end of game
         if len(self.history) > 1:
             if self.history[-1] is PASS_MOVE and self.history[-2] is PASS_MOVE \
-                and self.current_player == WHITE:
+                    and self.current_player == WHITE:
                 self.is_end_of_game = True
         return self.is_end_of_game
 
diff --git a/AlphaGo/mcts.py b/AlphaGo/mcts.py
index 962b4aabe..09f9439b8 100644
--- a/AlphaGo/mcts.py
+++ b/AlphaGo/mcts.py
@@ -11,6 +11,7 @@ class TreeNode(object):
     """A node in the MCTS tree. Each node keeps track of its own value Q, prior probability P, and
     its visit-count-adjusted prior score u.
     """
+
     def __init__(self, parent, prior_p):
         self._parent = parent
         self._children = {}  # a map from action to TreeNode
diff --git a/AlphaGo/models/nn_util.py b/AlphaGo/models/nn_util.py
index 2f29211ae..dcaabfd3e 100644
--- a/AlphaGo/models/nn_util.py
+++ b/AlphaGo/models/nn_util.py
@@ -118,6 +118,7 @@ class Bias(Layer):
     Largely copied from the keras docs:
     http://keras.io/layers/writing-your-own-keras-layers/#writing-your-own-keras-layers
     """
+
     def __init__(self, **kwargs):
         super(Bias, self).__init__(**kwargs)
 
diff --git a/AlphaGo/preprocessing/preprocessing.py b/AlphaGo/preprocessing/preprocessing.py
index 12f9269ea..5455e3e9b 100644
--- a/AlphaGo/preprocessing/preprocessing.py
+++ b/AlphaGo/preprocessing/preprocessing.py
@@ -2,7 +2,7 @@
 import AlphaGo.go as go
 
 ##
-## individual feature functions (state --> tensor) begin here
+# individual feature functions (state --> tensor) begin here
 ##
 
 
diff --git a/AlphaGo/training/reinforcement_policy_trainer.py b/AlphaGo/training/reinforcement_policy_trainer.py
index 8bf36cc94..18fac78a3 100644
--- a/AlphaGo/training/reinforcement_policy_trainer.py
+++ b/AlphaGo/training/reinforcement_policy_trainer.py
@@ -24,6 +24,7 @@ class BatchedReinforcementLearningSGD(Optimizer):
         lr: float >= 0. Learning rate.
         ng: int > 0. Number of games played in parallel. Each one has its own cumulative gradient.
     '''
+
     def __init__(self, lr=0.01, ng=20, **kwargs):
         super(BatchedReinforcementLearningSGD, self).__init__(**kwargs)
         self.__dict__.update(locals())
diff --git a/interface/Play.py b/interface/Play.py
index 58e3fe56d..059a5e06e 100644
--- a/interface/Play.py
+++ b/interface/Play.py
@@ -4,6 +4,7 @@
 
 class play_match(object):
     """Interface to handle play between two players."""
+
     def __init__(self, player1, player2, save_dir=None, size=19):
         # super(ClassName, self).__init__()
         self.player1 = player1
@@ -19,7 +20,7 @@ def _play(self, player):
         # self.state.write_to_disk()
         if len(self.state.history) > 1:
             if self.state.history[-1] is None and self.state.history[-2] is None \
-                and self.state.current_player == -1:
+                    and self.state.current_player == -1:
                 end_of_game = True
             else:
                 end_of_game = False