diff --git a/tianshou/data/advantage_estimation.py b/tianshou/data/advantage_estimation.py
index 3b6d930..8026c7e 100644
--- a/tianshou/data/advantage_estimation.py
+++ b/tianshou/data/advantage_estimation.py
@@ -216,7 +216,7 @@ class nstep_q_return:
         self.use_target_network = use_target_network
         self.discount_factor = discount_factor
 
-    def __call__(self, buffer, indexes=None):
+    def __call__(self, buffer, indexes=None, my_feed_dict={}):
         """
         :param buffer: A :class:`tianshou.data.data_buffer`.
         :param indexes: Optional. Indexes of data points on which the full return should be computed.
@@ -249,9 +249,9 @@ class nstep_q_return:
                         state = episode[last_frame_index + 1][STATE]
                         if self.use_target_network:
                             # [None] adds one dimension to the beginning
-                            qpredict = self.action_value.eval_value_all_actions_old(state[None])
+                            qpredict = self.action_value.eval_value_all_actions_old(state[None], my_feed_dict=my_feed_dict)
                         else:
-                            qpredict = self.action_value.eval_value_all_actions(state[None])
+                            qpredict = self.action_value.eval_value_all_actions(state[None], my_feed_dict=my_feed_dict)
                         target_q += current_discount_factor * max(qpredict[0])
                     episode_q.append(target_q)
 
diff --git a/tianshou/data/data_collector.py b/tianshou/data/data_collector.py
index 67d346c..ea5db51 100644
--- a/tianshou/data/data_collector.py
+++ b/tianshou/data/data_collector.py
@@ -118,7 +118,7 @@ class DataCollector(object):
 
         return
 
-    def next_batch(self, batch_size, standardize_advantage=True):
+    def next_batch(self, batch_size, standardize_advantage=True, my_feed_dict={}):
         """
         Constructs and returns the feed_dict of data to be used with ``sess.run``.
 
@@ -133,7 +133,7 @@ class DataCollector(object):
         sampled_index = self.data_buffer.sample(batch_size)
         if self.process_mode == 'sample':
             for processor in self.process_functions:
-                self.data_batch.update(processor(self.data_buffer, indexes=sampled_index))
+                self.data_batch.update(processor(self.data_buffer, indexes=sampled_index, my_feed_dict=my_feed_dict))
 
         # flatten rank-2 list to numpy array, construct feed_dict
         feed_dict = {}