Added Batch setting to active learning

ml-agents/mlagents/trainers/active_learning.py

         return MultivariateNormal(mean_x, covar_x)
 
 class ActiveLearningTaskSampler(object):
-    def __init__(self,ranges, warmup_steps=30, capacity=600, num_mc=500, beta=1.96, raw_samples=128, num_restarts=1):
+    def __init__(self,ranges, warmup_steps:int=30, capacity:int=600, num_mc:int=500, beta:float=1.96, raw_samples:int=128, num_restarts:int=1, num_batch:int=16):
         self.ranges = ranges
         self.warmup_steps = warmup_steps
         self.capacity = capacity
+        self.num_batch = num_batch
         self.num_restarts = num_restarts
         self.xdim = ranges.shape[0] + 1
         self.model = None
             self.model.set_train_data(self.X, self.Y)
             # self.model = self.model.condition_on_observations(new_X, new_Y)  # TODO: might be faster than setting the data need to test
 
-    def get_design_points(self, num_points:int=1, time=None):
+    def get_design_points(self, num_points:int=1, time=None, get_batch=True):
+        if get_batch:
+            num_points = min(num_points, self.num_batch)
         if not self.model or time < self.warmup_steps:
             return sample_random_points(self.bounds, num_points)
         

ml-agents/mlagents/trainers/settings.py

     beta:float=1.96
     raw_samples:int=128
     num_restarts:int=1
+    num_batch:int=16
 
 @attr.s(auto_attribs=True)
 class TaskParameterSettings:

ml-agents/mlagents/trainers/task_manager.py

 from mlagents_envs.logging_util import get_logger
 
 from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
-from mlagents.trainers.active_learning import ActiveLearningTaskSampler
+from mlagents.trainers.active_learning import ActiveLearningTaskSampler, sample_random_points
 
 logger = get_logger(__name__)
 
                 self._taskSamplers[behavior_name] = ActiveLearningTaskSampler(task_ranges, 
                     warmup_steps=active_hyps.warmup_steps, capacity=active_hyps.capacity,
                     num_mc=active_hyps.num_mc, beta=active_hyps.beta,
-                    raw_samples=active_hyps.raw_samples, num_restarts=active_hyps.num_restarts
+                    raw_samples=active_hyps.raw_samples, num_restarts=active_hyps.num_restarts,
+                    num_batch=active_hyps.num_batch
-                self._taskSamplers[behavior_name] = lambda n: uniform_sample(task_ranges, n)
+                self._taskSamplers[behavior_name] = lambda n: sample_random_points(task_ranges.T, n)
         self.t = {name: 0.0 for name in self.behavior_names}    
 
     def _make_task(self, behavior_name, tau):