advantage estimation function all take my_feed_dict (all examples runnable); such requirement should be made a signature

.gitignore

@@ -11,3 +11,4 @@ data
 !tianshou/data
 .log
 go-*
+*.egg-info/

tianshou/data/advantage_estimation.py

@@ -15,7 +15,7 @@ DONE = 3
 
 
 # TODO: add discount_factor... maybe make it to be a global config?
-def full_return(buffer, indexes=None):
+def full_return(buffer, indexes=None, my_feed_dict={}):
     """
     Naively compute full undiscounted return on episodic data, :math:`G_t = \sum_{t=0}^T r_t`.
     This function will print a warning when some of the episodes
@@ -80,7 +80,7 @@ class nstep_return:
         self.return_advantage = return_advantage
         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 specified return should be computed.
@@ -153,7 +153,7 @@ class ddpg_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 specified return should be computed.