# Why I got the same action when I train A2C when I increase the number of episodes?

I'm working on an advantage actor-critic (A2C) reinforcement learning model but the problem when I trained the system for 3500 episodes, I start to get the same action for all my testing results. While if I trained the system for less than 850 episodes, I got different actions. The value of state is always different and around 850 episodes the loss becomes zero.

  def take_action(self, state):
"""Take the action"""
action_probs = self.actor.predict(state)
action = np.random.choice(np.arange(len(action_probs)), p=action_probs)
return action


This the actor.predict function

def predict(self, s):
return self._sess.run(self._action_probs, {self._state: s})


Any Idea what causing this?