fix clipped ppo

agents/agent.py

@@ -338,6 +338,17 @@ class Agent(object):
             reward = max(reward, self.tp.env.reward_clipping_min)
         return reward
 
+    def tf_input_state(self, curr_state):
+        """
+        convert curr_state into input tensors tensorflow is expecting.
+        """
+        # add batch axis with length 1 onto each value
+        # extract values from the state based on agent.input_types
+        input_state = {}
+        for input_name in self.tp.agent.input_types.keys():
+            input_state[input_name] = np.expand_dims(np.array(curr_state[input_name]), 0)
+        return input_state
+
     def act(self, phase=RunPhase.TRAIN):
         """
         Take one step in the environment according to the network prediction and store the transition in memory