applied formatter

tools.py

@@ -122,7 +122,18 @@ class Logger:
         self._writer.add_video(name, value, step, 16)
 
 
-def simulate(agent, envs, cache, directory, logger, is_eval=False, limit=None, steps=0, episodes=0, state=None):
+def simulate(
+    agent,
+    envs,
+    cache,
+    directory,
+    logger,
+    is_eval=False,
+    limit=None,
+    steps=0,
+    episodes=0,
+    state=None,
+):
     # initialize or unpack simulation state
     if state is None:
         step, episode = 0, 0
@@ -200,7 +211,7 @@ def simulate(agent, envs, cache, directory, logger, is_eval=False, limit=None, s
                     logger.scalar(f"train_episodes", len(cache))
                     logger.write(step=logger.step)
                 else:
-                    if not 'eval_lengths' in locals():
+                    if not "eval_lengths" in locals():
                         eval_lengths = []
                         eval_scores = []
                         eval_done = False
@@ -278,6 +289,7 @@ class CollectDataset:
         self.add_to_cache(transition)
         return obs
 
+
 def add_to_cache(cache, id, transition):
     if id not in cache:
         cache[id] = dict()
@@ -292,6 +304,7 @@ def add_to_cache(cache, id, transition):
             else:
                 cache[id][key].append(convert(val))
 
+
 def erase_over_episodes(cache, dataset_size):
     step_in_dataset = 0
     for key, ep in reversed(sorted(cache.items(), key=lambda x: x[0])):
@@ -304,6 +317,7 @@ def erase_over_episodes(cache, dataset_size):
             del cache[key]
     return step_in_dataset
 
+
 def convert(value, precision=32):
     value = np.array(value)
     if np.issubdtype(value.dtype, np.floating):
@@ -318,6 +332,7 @@ def convert(value, precision=32):
         raise NotImplementedError(value.dtype)
     return value.astype(dtype)
 
+
 def save_episodes(directory, episodes):
     directory = pathlib.Path(directory).expanduser()
     directory.mkdir(parents=True, exist_ok=True)