Answers.Body: <p>Indeed balancing the contributions of the actor loss and critic loss is a common strategy in actor-critic methods which ensures that the gradients from both the actor and critic are of similar magnitude and contribute equally to the updates of the shared feature extraction layers during training. Without balancing factor, the critic loss usually dominates especially during the early training stage due to the inherent clipped surrogate loss of PPO's actor. You can see the codebase of ppo.py in stable_baselines3 as follows:</p>
  <p><code> loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss</code></p>
  <p>, where <code>vf_coef: float = 0.5</code> is the balancing factor for critic's state value function loss <code>value_loss</code>.</p>
  
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Body: <p>I am implementing a PPO-based reinforcement learning model, where both the actor and critic networks share the same feature extraction layers. This is similar to the setup used in Stable Baselines3.</p>
  <p>However, since the total loss is calculated as the sum of the actor loss and the critic loss, and these two losses are computed using different formulas, their magnitudes might differ significantly. This could lead to differences in the gradients that are backpropagated through the shared layers, which might affect the optimization process.</p>
  <p>Given this, my question is: Should I introduce an additional weight when calculating the total loss in order to balance the contributions of the actor loss and the critic loss to the shared layers, ensuring both losses have a similar impact on the gradients?</p>
  <p>Any advice or best practices on how to handle this would be greatly appreciated!</p>
  
Tags: <reinforcement-learning><proximal-policy-optimization>
Title: How to balance actor loss and critic loss for shared feature extraction layers in PPO?
UserDisplayName: Weitao Kang
ThreadText: # How to balance actor loss and critic loss for shared feature extraction layers in PPO?
  
  (Asked by: username_0 on 2024-12-03)
  
  I am implementing a PPO-based reinforcement learning model, where both the actor and critic networks share the same feature extraction layers. This is similar to the setup used in Stable Baselines3.
  
  However, since the total loss is calculated as the sum of the actor loss and the critic loss, and these two losses are computed using different formulas, their magnitudes might differ significantly. This could lead to differences in the gradients that are backpropagated through the shared layers, which might affect the optimization process.
  
  Given this, my question is: Should I introduce an additional weight when calculating the total loss in order to balance the contributions of the actor loss and the critic loss to the shared layers, ensuring both losses have a similar impact on the gradients?
  
  Any advice or best practices on how to handle this would be greatly appreciated!
  
  **Tags:** reinforcement-learning, proximal-policy-optimization
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-12-03. Score: 0
  
  Indeed balancing the contributions of the actor loss and critic loss is a common strategy in actor-critic methods which ensures that the gradients from both the actor and critic are of similar magnitude and contribute equally to the updates of the shared feature extraction layers during training. Without balancing factor, the critic loss usually dominates especially during the early training stage due to the inherent clipped surrogate loss of PPO's actor. You can see the codebase of ppo.py in stable\_baselines3 as follows:
  
  `loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss`
  
  , where `vf_coef: float = 0.5` is the balancing factor for critic's state value function loss `value_loss`.
  
  
  ---
  
  