<p>I am also very curious about this. I have been implementing A2C in PyTorch from scratch and have tried both a single optimizer and separate optimizers. The separate case learns so much quicker. I believe it may have something to do with the coefficients, balancing the critic coefficient so the two losses were within a similar range seemed to work.</p>

CC BY-SA 4.0
2022-02-26T14:36:33.953Z
2022-02-26T14:36:33.953Z
Elfurd
<p>Say that I have a simple Actor-Critic architecture, (I am not familiar with Tensorflow, but) in Pytorch we need to specify the parameters when defining an optimizer (SGD, Adam, etc) and therefore we can define 2 separate optimizers for the Actor and the Critic and the backward process will be</p>
<pre><code>actor_loss.backward()
actor_optimizer.step()
critic_loss.backward()
critic_optimizer.step()
</code></pre>
<p>or we can use a single optimizer for both the Actor's and the Critic's parameters so the backward process can be like</p>
<pre><code>loss = actor_loss + critic_loss
loss.backward()
optimizer.step()
</code></pre>
<p>I have 2 questions regarding both approaches:</p>
<ol>
<li><p>Is there any consideration (pros, cons) for both the single joined optimizer and the separate optimizer approach?</p>
</li>
<li><p>If I want to save the best Agent (Actor and Critic) periodically (based on a predefined testing environment), do I always have to update the Critic, regardless of the current Agent's performance? Because (CMIIW) the Critic is (in its most basic purpose) only for predicting the action-value or state-value thus a more trained Critic is better.</p>
</li>
</ol>

CC BY-SA 4.0
2021-10-17T02:38:19.243Z
You're asking 2 distinct questions here. I would suggest that you ask the second question in a separate post.
nbro
<reinforcement-learning><deep-rl><pytorch><actor-critic-methods><optimizers>
Joined vs Separate optimizer for Actor-Critic
Sanyou
# Joined vs Separate optimizer for Actor-Critic

(Asked by: username_0 on 2021-09-11)

Say that I have a simple Actor-Critic architecture, (I am not familiar with Tensorflow, but) in Pytorch we need to specify the parameters when defining an optimizer (SGD, Adam, etc) and therefore we can define 2 separate optimizers for the Actor and the Critic and the backward process will be
actor_loss.backward()
actor_optimizer.step()
critic_loss.backward()
critic_optimizer.step()

or we can use a single optimizer for both the Actor's and the Critic's parameters so the backward process can be like
loss = actor_loss + critic_loss
loss.backward()
optimizer.step()

I have 2 questions regarding both approaches:

Is there any consideration (pros, cons) for both the single joined optimizer and the separate optimizer approach?

If I want to save the best Agent (Actor and Critic) periodically (based on a predefined testing environment), do I always have to update the Critic, regardless of the current Agent's performance? Because (CMIIW) the Critic is (in its most basic purpose) only for predicting the action-value or state-value thus a more trained Critic is better.

**Tags:** reinforcement-learning, deep-rl, pytorch, actor-critic-methods, optimizers

> **Comments:**
> **username_1**: You're asking 2 distinct questions here. I would suggest that you ask the second question in a separate post.  

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## 1 answers

### Answer by username_2 on 2022-02-26. Score: 1

I am also very curious about this. I have been implementing A2C in PyTorch from scratch and have tried both a single optimizer and separate optimizers. The separate case learns so much quicker. I believe it may have something to do with the coefficients, balancing the critic coefficient so the two losses were within a similar range seemed to work.


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