Fix for CC models w/ RNN and Curiosity (#860)

python/unitytrainers/ppo/trainer.py

         feed_dict = {self.model.batch_size: len(curr_brain_info.vector_observations),
                      self.model.sequence_length: 1}
         if self.use_recurrent:
-            feed_dict[self.model.prev_action] = curr_brain_info.previous_vector_actions.flatten()
+            if not self.is_continuous_action:
+                feed_dict[self.model.prev_action] = curr_brain_info.previous_vector_actions.flatten()
             if curr_brain_info.memories.shape[1] == 0:
                 curr_brain_info.memories = np.zeros((len(curr_brain_info.agents), self.m_size))
             feed_dict[self.model.memory_in] = curr_brain_info.memories
         if self.use_curiosity:
             feed_dict = {self.model.batch_size: len(curr_info.vector_observations), self.model.sequence_length: 1}
             if self.is_continuous_action:
-                feed_dict[self.model.output] = next_info.previous_vector_actions.flatten()
+                feed_dict[self.model.output] = next_info.previous_vector_actions
             else:
                 feed_dict[self.model.action_holder] = next_info.previous_vector_actions.flatten()
             if self.use_visual_obs:
             if self.use_vector_obs:
                 feed_dict[self.model.vector_in] = curr_info.vector_observations
                 feed_dict[self.model.next_vector_in] = next_info.vector_observations
-
+            if self.use_recurrent:
+                if curr_info.memories.shape[1] == 0:
+                    curr_info.memories = np.zeros((len(curr_info.agents), self.m_size))
+                feed_dict[self.model.memory_in] = curr_info.memories
             intrinsic_rewards = self.sess.run(self.model.intrinsic_reward,
                                               feed_dict=feed_dict) * float(self.has_updated)
             return intrinsic_rewards