PEP-8 Fixes in algorithmic environment (#1382)

Remove trailing whitespaces.
Make line breaks adhere to 80 character limit (not all, but quite a few).
Remove unused imports.
Other miscellaneous PEP-8 fixes.

gym/envs/algorithmic/algorithmic_env.py

@@ -26,7 +26,7 @@ Reward schedule:
     otherwise: 0
 
 In the beginning, input strings will be fairly short. After an environment has
-been consistently solved over some window of episodes, the environment will 
+been consistently solved over some window of episodes, the environment will
 increase the average length of generated strings. Typical env specs require
 leveling up many times to reach their reward threshold.
 """
@@ -42,16 +42,16 @@ from six import StringIO
 class AlgorithmicEnv(Env):
 
     metadata = {'render.modes': ['human', 'ansi']}
-    # Only 'promote' the length of generated input strings if the worst of the 
+    # Only 'promote' the length of generated input strings if the worst of the
     # last n episodes was no more than this far from the maximum reward
     MIN_REWARD_SHORTFALL_FOR_PROMOTION = -1.0
 
     def __init__(self, base=10, chars=False, starting_min_length=2):
         """
-        base: Number of distinct characters. 
+        base: Number of distinct characters.
         chars: If True, use uppercase alphabet. Otherwise, digits. Only affects
                rendering.
-        starting_min_length: Minimum input string length. Ramps up as episodes 
+        starting_min_length: Minimum input string length. Ramps up as episodes
                              are consistently solved.
         """
         self.base = base
@@ -59,15 +59,15 @@ class AlgorithmicEnv(Env):
         self.last = 10
         # Cumulative reward earned this episode
         self.episode_total_reward = None
-        # Running tally of reward shortfalls. e.g. if there were 10 points to earn and
+        # Running tally of reward shortfalls. e.g. if there were 10 points to
-        # we got 8, we'd append -2
+        # earn and we got 8, we'd append -2
         AlgorithmicEnv.reward_shortfalls = []
         if chars:
             self.charmap = [chr(ord('A')+i) for i in range(base)]
         else:
             self.charmap = [str(i) for i in range(base)]
         self.charmap.append(' ')
-        # TODO: Not clear why this is a class variable rather than instance. 
+        # TODO: Not clear why this is a class variable rather than instance.
         # Could lead to some spooky action at a distance if someone is working
         # with multiple algorithmic envs at once. Also makes testing tricky.
         AlgorithmicEnv.min_length = starting_min_length
@@ -78,7 +78,8 @@ class AlgorithmicEnv(Env):
         self.action_space = Tuple(
             [Discrete(len(self.MOVEMENTS)), Discrete(2), Discrete(self.base)]
         )
-        # Can see just what is on the input tape (one of n characters, or nothing)
+        # Can see just what is on the input tape (one of n characters, or
+        # nothing)
         self.observation_space = Discrete(self.base + 1)
         self.seed()
         self.reset()
@@ -170,10 +171,11 @@ class AlgorithmicEnv(Env):
             try:
                 correct = pred == self.target[self.write_head_position]
             except IndexError:
-                logger.warn("It looks like you're calling step() even though this "+
+                logger.warn(
-                    "environment has already returned done=True. You should always call "+
+                    "It looks like you're calling step() even though this "
-                    "reset() once you receive done=True. Any further steps are undefined "+
+                    "environment has already returned done=True. You should "
-                    "behaviour.")
+                    "always call reset() once you receive done=True. Any "
+                    "further steps are undefined behaviour.")
                 correct = False
             if correct:
                 reward = 1.0
@@ -201,7 +203,7 @@ class AlgorithmicEnv(Env):
         return self.input_width + len(self.target) + 4
 
     def _check_levelup(self):
-        """Called between episodes. Update our running record of episode rewards 
+        """Called between episodes. Update our running record of episode rewards
         and, if appropriate, 'level up' minimum input length."""
         if self.episode_total_reward is None:
             # This is before the first episode/call to reset(). Nothing to do
@@ -209,11 +211,10 @@ class AlgorithmicEnv(Env):
         AlgorithmicEnv.reward_shortfalls.append(self.episode_total_reward - len(self.target))
         AlgorithmicEnv.reward_shortfalls = AlgorithmicEnv.reward_shortfalls[-self.last:]
         if len(AlgorithmicEnv.reward_shortfalls) == self.last and \
-          min(AlgorithmicEnv.reward_shortfalls) >= self.MIN_REWARD_SHORTFALL_FOR_PROMOTION and \
+                min(AlgorithmicEnv.reward_shortfalls) >= self.MIN_REWARD_SHORTFALL_FOR_PROMOTION and \
-          AlgorithmicEnv.min_length < 30:
+                AlgorithmicEnv.min_length < 30:
             AlgorithmicEnv.min_length += 1
             AlgorithmicEnv.reward_shortfalls = []
-        
 
     def reset(self):
         self._check_levelup()
@@ -258,13 +259,13 @@ class TapeAlgorithmicEnv(AlgorithmicEnv):
             return self.input_data[pos]
         except IndexError:
             return self.base
-    
+
     def generate_input_data(self, size):
         return [self.np_random.randint(self.base) for _ in range(size)]
 
     def render_observation(self):
         x = self.read_head_position
-        x_str =      "Observation Tape    : "
+        x_str = "Observation Tape    : "
         for i in range(-2, self.input_width + 2):
             if i == x:
                 x_str += colorize(self._get_str_obs(np.array([i])), 'green', highlight=True)
@@ -278,6 +279,7 @@ class GridAlgorithmicEnv(AlgorithmicEnv):
     """An algorithmic env with a 2-d input grid."""
     MOVEMENTS = ['left', 'right', 'up', 'down']
     READ_HEAD_START = (0, 0)
+
     def __init__(self, rows, *args, **kwargs):
         self.rows = rows
         AlgorithmicEnv.__init__(self, *args, **kwargs)
@@ -316,7 +318,7 @@ class GridAlgorithmicEnv(AlgorithmicEnv):
 
     def render_observation(self):
         x = self.read_head_position
-        label =      "Observation Grid    : "
+        label = "Observation Grid    : "
         x_str = ""
         for j in range(-1, self.rows+1):
             if j != -1:

gym/envs/algorithmic/copy_.py

@@ -4,10 +4,10 @@ the output tape. http://arxiv.org/abs/1511.07275
 """
 from gym.envs.algorithmic import algorithmic_env
 
+
 class CopyEnv(algorithmic_env.TapeAlgorithmicEnv):
     def __init__(self, base=5, chars=True):
         super(CopyEnv, self).__init__(base=base, chars=chars)
 
     def target_from_input_data(self, input_data):
         return input_data
-

gym/envs/algorithmic/duplicated_input.py

@@ -5,6 +5,7 @@ http://arxiv.org/abs/1511.07275
 from __future__ import division
 from gym.envs.algorithmic import algorithmic_env
 
+
 class DuplicatedInputEnv(algorithmic_env.TapeAlgorithmicEnv):
     def __init__(self, duplication=2, base=5):
         self.duplication = duplication

gym/envs/algorithmic/repeat_copy.py

@@ -4,12 +4,13 @@ the output tape. http://arxiv.org/abs/1511.07275
 """
 from gym.envs.algorithmic import algorithmic_env
 
+
 class RepeatCopyEnv(algorithmic_env.TapeAlgorithmicEnv):
     MIN_REWARD_SHORTFALL_FOR_PROMOTION = -.1
+
     def __init__(self, base=5):
         super(RepeatCopyEnv, self).__init__(base=base, chars=True)
         self.last = 50
 
     def target_from_input_data(self, input_data):
         return input_data + list(reversed(input_data)) + input_data
-

gym/envs/algorithmic/reverse.py

@@ -2,11 +2,12 @@
 Task is to reverse content over the input tape.
 http://arxiv.org/abs/1511.07275
 """
-
 from gym.envs.algorithmic import algorithmic_env
 
+
 class ReverseEnv(algorithmic_env.TapeAlgorithmicEnv):
     MIN_REWARD_SHORTFALL_FOR_PROMOTION = -.1
+
     def __init__(self, base=2):
         super(ReverseEnv, self).__init__(base=base, chars=True, starting_min_length=1)
         self.last = 50

gym/envs/algorithmic/reversed_addition.py

@@ -1,7 +1,7 @@
 from __future__ import division
-import numpy as np
 from gym.envs.algorithmic import algorithmic_env
 
+
 class ReversedAdditionEnv(algorithmic_env.GridAlgorithmicEnv):
     def __init__(self, rows=2, base=3):
         super(ReversedAdditionEnv, self).__init__(rows=rows, base=base, chars=False)