write to csv

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

 
 from mlagents.trainers.stats import StatsSummary
 
+
 class PPOOptimizer(TFOptimizer):
     def __init__(self, policy: TFPolicy, trainer_params: Dict[str, Any]):
         """
             update_stats[stat_name] = update_vals[update_name]
         return update_stats
 
-    def compute_input_sensitivity(self, batch: AgentBuffer, num_sequences: int) -> Dict[int, float]:
+    def compute_input_sensitivity(
+        self, batch: AgentBuffer, num_sequences: int
+    ) -> Dict[int, float]:
-        out = dict((obs,StatsSummary(grad, 0.0, 0.0)) for obs, grad in enumerate(sens))
+        out = dict((obs, StatsSummary(grad, 0.0, 0.0)) for obs, grad in enumerate(sens))
         for obs, grad in sorted(enumerate(sens), reverse=True, key=lambda x: x[1]):
             print("Observation {} has relevance {}".format(obs, grad))
         return out

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

             self.update_buffer, self.policy.sequence_length
         )
         self.csv_writer.write_stats("sensitivity", sensitivities, self.step)
-        
+
         for _ in range(num_epoch):
             self.update_buffer.shuffle(sequence_length=self.policy.sequence_length)
             buffer = self.update_buffer