Formatting

ml-agents/mlagents/trainers/ppo/trainer.py

         )
         for name, reward_signal in self.optimizer.reward_signals.items():
             evaluate_result = (
-                reward_signal.evaluate(agent_buffer_trajectory)
-                * reward_signal.strength
+                reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength
             )
             agent_buffer_trajectory[f"{name}_rewards"].extend(evaluate_result)
             # Report the reward signals

ml-agents/mlagents/trainers/sac/trainer.py

         )
         for name, reward_signal in self.optimizer.reward_signals.items():
             evaluate_result = (
-                reward_signal.evaluate(agent_buffer_trajectory)
-                * reward_signal.strength
+                reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength
             )
 
             # Report the reward signals
             agent_buffer_trajectory, trajectory.next_obs, trajectory.done_reached
         )
         for name, v in value_estimates.items():
-                self._stats_reporter.add_stat(
-                    f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value",
-                    np.mean(v),
-                )
+            self._stats_reporter.add_stat(
+                f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value",
+                np.mean(v),
+            )
 
         # Bootstrap using the last step rather than the bootstrap step if max step is reached.
         # Set last element to duplicate obs and remove dones.

ml-agents/tests/yamato/yamato_utils.py

 
     # Set up the venv and install mlagents
     subprocess.check_call(f"python -m venv {venv_path}", shell=True)
-    pip_commands = [
-        "--upgrade pip",
-        "--upgrade setuptools",
-    ]
+    pip_commands = ["--upgrade pip", "--upgrade setuptools"]
     if mlagents_python_version:
         # install from pypi
         pip_commands += [