Added num repeat parameter for tasks

ml-agents/mlagents/trainers/settings.py

     
     parameters: Dict[str, UniformSettings]
     active_learning: Optional[ActiveLearnerSettings] = None
+    repeat:int=8
 
     @staticmethod
     def structure(d: Mapping, t: type) -> Dict[str, "TaskParameterSettings"]:

ml-agents/mlagents/trainers/task_manager.py

         self.param_names = {name: list(self._dict_settings[name].parameters.keys()) for name in self.behavior_names}
         self._taskSamplers = {}
         self.report_buffer = []
+        self.num_repeat = {name: 1 for name in self.behavior_names}
 
         for behavior_name in self.behavior_names:
             lows = []
                 lows.append(low)
                 highs.append(high)    
             task_ranges = torch.tensor([lows, highs]).float().T
+            num_repeat = self._dict_settings[behavior_name].num_repeat
+            self.num_repeat[behavior_name] = num_repeat
             active_hyps = self._dict_settings[behavior_name].active_learning
             if active_hyps:
                 self._taskSamplers[behavior_name] = ActiveLearningTaskSampler(task_ranges, 
             else:
                 self._taskSamplers[behavior_name] = lambda n: sample_random_points(task_ranges.T, n)
         self.t = {name: 0.0 for name in self.behavior_names}    
+        self.counter = {name: 0 for name in self.behavior_names}    
 
     def _make_task(self, behavior_name, tau):
         task = {}
         # print("sampled taus", current_time, taus)
         tasks = [self._make_task(behavior_name, tau) for tau in taus]
         self.report_buffer.extend(tasks)
-        return tasks
+        tasks_repeated = []
+        for i in range(self.num_repeat[behavior_name]):
+            tasks_repeated.extend(tasks)
+        return tasks_repeated
 
     def update(self, behavior_name: str, task_perfs: List[Tuple[Dict, float]]
     ) -> Tuple[bool, bool]:
 
             X = torch.stack(taus, dim=0)
             Y = torch.tensor(perfs).float().reshape(-1, 1)
-            self._taskSamplers[behavior_name].update_model(X, Y, refit=True)
+            N = len(task_perfs)
+            self.counter[behavior_name] += N
+            if self.counter[behavior_name] >= self.num_repeat:
+                refit = True
+                self.counter[behavior_name] = 0
+            else:
+                refit = False
+            self._taskSamplers[behavior_name].update_model(X, Y, refit=refit)
         
         return updated, must_reset