title: How do shared parameters in actor-critic models work?
body: <p>Hello StackOverflow Community!</p>
  
  <p>I have a question about Actor-Critic Models in Reinforcement Learning.</p>
  
  <p>While listening policy gradient methods classes of Berkeley University, it is said in the lecture that in the actor-critic algorithms where we both optimize our policy with some policy parameters and our value functions with some value function parameters, we use same parameters in both optimization problems(i.e. policy parameters = value function parameters) in some algorithms (e.g. A2C/A3C)</p>
  
  <p>I could not understand how this works. I was thinking that we should optimize them separately. How does this shared parameter solution helps us?</p>
  
  <p>Thanks in advance :)</p>