Answers.Body: <p>If your model can identify sparse and dense regions, you can continue to use Gaussian noise before softmax activation in dense regions where the agent has many options so Gaussian noise can help refine the policy by introducing gentle small randomness. In sparse regions you can try <a href="https://en.wikipedia.org/wiki/Gumbel_distribution" rel="nofollow noreferrer">Gumbel</a> noise before softmax activation where blind gentle random exploration as in the usual case of Gaussian noise would likely waste time, and the structured randomness of Gumbel noise encourages diverse extreme exploration more efficiently.</p>
  <blockquote>
  <p>the Gumbel distribution (also known as the type-I generalized extreme value distribution) is used to model the distribution of the maximum (or the minimum) of a number of samples of various distributions... This distribution might be used to represent the distribution of the maximum level of a river in a particular year if there was a list of maximum values for the past ten years. It is useful in predicting the chance that an extreme earthquake, flood or other natural disaster will occur.</p>
  </blockquote>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-12-05T08:18:53.597Z
Answers.LastActivityDate: 2024-12-05T08:18:53.597Z
Answers.UserDisplayName: cinch
Body: <p>I'm implementing a RL agent that navigates a graph. I'm using a softmax activation in the final layer of the actor network to model the action probabilities.</p>
  <p>To encourage exploration during training, I need to introduce noise into the action selection process, but I'm unsure about the most appropriate type of noise to use given the softmax output.</p>
  <p>The graph has both densely and sparsely connected regions, and I want the agent to explore alternate paths without making overly random decisions.</p>
  <p>What type of noise would work best in this scenario? Are there specific advantages or implementation details I should consider for these noise types?</p>
  <p>For now I have been using Gaussian noise that is applied to the state before the activation.</p>
  <p>Any advice or insights would be appreciated!</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-04T17:00:59.663Z
Comments.Text: Welcome to AI Stack Exchange. Could you please clarify (use [edit] to add to the question) which RL method you are using. The nature of exploration, and whether adding noise is helpful or necessary, depends on which of the many RL approaches you use. E.g. is this project using SARSA, Q-Learning, REINFORCE, A3C, DDPG or one of the many other RL approches?
Comments.UserDisplayName: Neil Slater
Tags: <reinforcement-learning><deep-learning>
Title: What type of noise should I use with softmax activation?
UserDisplayName: Baki
ThreadText: # What type of noise should I use with softmax activation?
  
  (Asked by: username_0 on 2024-12-04)
  
  I'm implementing a RL agent that navigates a graph. I'm using a softmax activation in the final layer of the actor network to model the action probabilities.
  
  To encourage exploration during training, I need to introduce noise into the action selection process, but I'm unsure about the most appropriate type of noise to use given the softmax output.
  
  The graph has both densely and sparsely connected regions, and I want the agent to explore alternate paths without making overly random decisions.
  
  What type of noise would work best in this scenario? Are there specific advantages or implementation details I should consider for these noise types?
  
  For now I have been using Gaussian noise that is applied to the state before the activation.
  
  Any advice or insights would be appreciated!
  
  **Tags:** reinforcement-learning, deep-learning
  > **Comments:**
  > **username_1**: Welcome to AI Stack Exchange. Could you please clarify (use [edit] to add to the question) which RL method you are using. The nature of exploration, and whether adding noise is helpful or necessary, depends on which of the many RL approaches you use. E.g. is this project using SARSA, Q-Learning, REINFORCE, A3C, DDPG or one of the many other RL approches?  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2024-12-05. Score: 0
  
  If your model can identify sparse and dense regions, you can continue to use Gaussian noise before softmax activation in dense regions where the agent has many options so Gaussian noise can help refine the policy by introducing gentle small randomness. In sparse regions you can try [Gumbel](https://en.wikipedia.org/wiki/Gumbel_distribution) noise before softmax activation where blind gentle random exploration as in the usual case of Gaussian noise would likely waste time, and the structured randomness of Gumbel noise encourages diverse extreme exploration more efficiently.
  
  > the Gumbel distribution (also known as the type-I generalized extreme value distribution) is used to model the distribution of the maximum (or the minimum) of a number of samples of various distributions... This distribution might be used to represent the distribution of the maximum level of a river in a particular year if there was a list of maximum values for the past ten years. It is useful in predicting the chance that an extreme earthquake, flood or other natural disaster will occur.
  
  
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