Answers.Body: <p>The actor's loss increasing is often a symptom of poor gradients being propagated from the critic. If the critic provides bad value estimates or is stuck predicting constant Q-values, the actor won't learn useful policies leading to increasing loss. Notice your critic's loss is very unstable even towards zero.</p>
  <p>Possibly if the exploration policy is not diverse enough or replay buffer isn't large enough , the critic does not see a wide range of state-action pairs limiting the quality of its Q-value estimates due to overfit a narrow range. In DDPG the actor learns by maximizing the Q-value predicted by the critic, therefore if the Q-value gradients are noisy or flat the actor's updates will become unstable or ineffective. Try to monitor the estimated Q-value.</p>
  <p>Also an imbalance in learning rates or update frequencies between the actor and critic can lead to this behavior, similarly if the target networks for both actor and critic are not soft-updated with <span class="math-container">$\tau$</span> properly, the learning dynamics can destabilize.</p>
  
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Answers.CreationDate: 2024-11-26T02:54:46.400Z
Answers.Text: thank you! i add two more pictures in which the critic loss is stable while the actor loss is increasing.i use the action noise and soft update,also i decrease the update frequencies of actor,the learning rate of actor and critic are 1 : 10. the problem  still exist,and i wonder whether this problem is caused by my dataset or reward function.
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  <p>when i train my DDPG ,the actor loss is getting higher and higher which should get lower and lower ,while the critic loss is decreasing to zero. i wonder why and how to prevent this situation. I apply DDPG to power distribution of electrical vehicle,when i use my dataset as power input,the ddpg will go bad,also the sac,but the reward will get higher while the actor loss is increasing.In another situation,the critic loss is stable and near to zero, but the actor loss is still increasing.<a href="https://i.sstatic.net/oTQZ6erA.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/oTQZ6erA.png" alt="enter image description here" /></a></p>
  
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Comments.CreationDate: 2024-11-21T11:15:07.153Z
Comments.Text: Can you provide more details about your problem? What are you applying DDPG to?
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Comments.CreationDate: 2024-11-26T02:50:05.943Z
Comments.Text: yes！i add some pictures to describe the same problem of actor loss, if you'd like ,please help me,thanks!
Comments.UserDisplayName: rlfish
Tags: <reinforcement-learning><ddpg>
Title: the actor loss is getting higher and higher in DDPG
UserDisplayName: rlfish
ThreadText: # the actor loss is getting higher and higher in DDPG
  
  (Asked by: username_0 on 2024-11-19)
  
  [![enter image description here](https://i.sstatic.net/kZESFc0b.png)](https://i.sstatic.net/kZESFc0b.png)[![enter image description here](https://i.sstatic.net/M6KvcwPp.png)](https://i.sstatic.net/M6KvcwPp.png)
  
  when i train my DDPG ,the actor loss is getting higher and higher which should get lower and lower ,while the critic loss is decreasing to zero. i wonder why and how to prevent this situation. I apply DDPG to power distribution of electrical vehicle,when i use my dataset as power input,the ddpg will go bad,also the sac,but the reward will get higher while the actor loss is increasing.In another situation,the critic loss is stable and near to zero, but the actor loss is still increasing.[![enter image description here](https://i.sstatic.net/oTQZ6erA.png)](https://i.sstatic.net/oTQZ6erA.png)
  
  **Tags:** reinforcement-learning, ddpg
  > **Comments:**
  > **username_1**: Can you provide more details about your problem? What are you applying DDPG to?  
  > **username_0**: yes！i add some pictures to describe the same problem of actor loss, if you'd like ,please help me,thanks!  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2024-11-19. Score: 0
  
  The actor's loss increasing is often a symptom of poor gradients being propagated from the critic. If the critic provides bad value estimates or is stuck predicting constant Q-values, the actor won't learn useful policies leading to increasing loss. Notice your critic's loss is very unstable even towards zero.
  
  Possibly if the exploration policy is not diverse enough or replay buffer isn't large enough , the critic does not see a wide range of state-action pairs limiting the quality of its Q-value estimates due to overfit a narrow range. In DDPG the actor learns by maximizing the Q-value predicted by the critic, therefore if the Q-value gradients are noisy or flat the actor's updates will become unstable or ineffective. Try to monitor the estimated Q-value.
  
  Also an imbalance in learning rates or update frequencies between the actor and critic can lead to this behavior, similarly if the target networks for both actor and critic are not soft-updated with $\tau$ properly, the learning dynamics can destabilize.
  
  > **Comments:**
  > **username_0**: thank you! i add two more pictures in which the critic loss is stable while the actor loss is increasing.i use the action noise and soft update,also i decrease the update frequencies of actor,the learning rate of actor and critic are 1 : 10. the problem  still exist,and i wonder whether this problem is caused by my dataset or reward function.  
  
  ---
  
  