Assert is a keyword, not a function

gym/envs/algorithmic/algorithmic_env.py

@@ -41,7 +41,7 @@ class AlgorithmicEnv(Env):
     def _get_obs(self, pos=None):
         if pos is None:
             pos = self.x
-        assert(isinstance(pos, np.ndarray) and pos.shape[0] == self.inp_dim)
+        assert isinstance(pos, np.ndarray) and pos.shape[0] == self.inp_dim
         if ha(pos) not in self.content:
             self.content[ha(pos)] = self.base
         return self.content[ha(pos)]
@@ -90,7 +90,7 @@ class AlgorithmicEnv(Env):
             x_str = label + x_str
             return x_str
         else:
-            assert(False)
+            assert False
 
 
     def _render(self, mode='human', close=False):

gym/envs/box2d/bipedal_walker.py

@@ -406,7 +406,7 @@ class BipedalWalker(gym.Env):
             1.0 if self.legs[3].ground_contact else 0.0
             ]
         state += [l.fraction for l in self.lidar]
-        assert(len(state)==24)
+        assert len(state)==24
 
         self.scroll = pos.x - VIEWPORT_W/SCALE/5
 

gym/envs/box2d/lunar_lander.py

@@ -263,7 +263,7 @@ class LunarLander(gym.Env):
             1.0 if self.legs[0].ground_contact else 0.0,
             1.0 if self.legs[1].ground_contact else 0.0
             ]
-        assert(len(state)==8)
+        assert len(state)==8
 
         reward = 0
         shaping = \

gym/envs/debugging/one_round_deterministic_reward.py

@@ -21,7 +21,7 @@ class OneRoundDeterministicRewardEnv(gym.Env):
         self._reset()
 
     def _step(self, action):
-        assert(self.action_space.contains(action))
+        assert self.action_space.contains(action)
         if action:
                 reward = 1
         else:

gym/envs/debugging/one_round_nondeterministic_reward.py

@@ -22,7 +22,7 @@ class OneRoundNondeterministicRewardEnv(gym.Env):
         self._reset()
 
     def _step(self, action):
-        assert(self.action_space.contains(action))
+        assert self.action_space.contains(action)
         if action:
             #your agent should figure out that this option has expected value 2.5
             reward = random.choice([0, 5])

gym/envs/debugging/two_round_deterministic_reward.py

@@ -28,7 +28,7 @@ class TwoRoundDeterministicRewardEnv(gym.Env):
     def _step(self, action):
         rewards = [[0, 3], [1, 2]]
 
-        assert(self.action_space.contains(action))
+        assert self.action_space.contains(action)
 
         if self.firstAction is None:
             self.firstAction = action

gym/envs/debugging/two_round_nondeterministic_reward.py

@@ -38,7 +38,7 @@ class TwoRoundNondeterministicRewardEnv(gym.Env):
             ]
         ]
 
-        assert(self.action_space.contains(action))
+        assert self.action_space.contains(action)
 
         if self.firstAction is None:
             self.firstAction = action

gym/envs/parameter_tuning/convergence.py

@@ -65,7 +65,7 @@ class ConvergenceControl(gym.Env):
         """
         Perform some action in the environment
         """
-        assert(self.action_space.contains(action))
+        assert self.action_space.contains(action)
 
         lr, decay, momentum, batch_size, l1, l2 = action;
 

gym/envs/parameter_tuning/train_deep_cnn.py

@@ -59,7 +59,7 @@ class CNNClassifierTraining(gym.Env):
         """
         Perform some action in the environment
         """
-        assert (self.action_space.contains(action))
+        assert self.action_space.contains(action)
 
         lr, decay, momentum, batch_size, l1, l2, convs, fcs = action
 

gym/envs/toy_text/blackjack.py

@@ -89,7 +89,7 @@ class BlackjackEnv(gym.Env):
         return [seed]
 
     def _step(self, action):
-        assert(self.action_space.contains(action))
+        assert self.action_space.contains(action)
         if action:  # hit: add a card to players hand and return
             self.player.append(draw_card(self.np_random))
             if is_bust(self.player):

gym/envs/toy_text/nchain.py

@@ -36,7 +36,7 @@ class NChainEnv(gym.Env):
         return [seed]
 
     def _step(self, action):
-        assert(self.action_space.contains(action))
+        assert self.action_space.contains(action)
         if self.np_random.rand() < self.slip:
             action = not action  # agent slipped, reverse action taken
         if action:  # 'backwards': go back to the beginning, get small reward