Answers.Body: <p>In the regular PPO formulation, which I don't believe is strictly following maximum-entropy RL, only the actor has the entropy term which is employed as a regularizer to favor exploration. (I guess adding <span class="math-container">$H$</span> to both actor and critic in PPO would result in a different agent.)</p>
  <p>Basically, since the actor parameterizes a probability distribution like a Gaussian, whose entropy has an analytical form, you need to just compute that and add a term (weighted by a coefficient) to the loss function. If you don't know how to compute <span class="math-container">$H$</span>, you can estimate it by taking the negative log-probability, averaging over the batch: <span class="math-container">$H[\pi]\approx -\frac1B\sum_{a,s\in B}-\log \pi(a\mid s)$</span>. Note: the first minus is to ensure the optimizer to maximize the entropy.</p>
  
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Answers.CreationDate: 2023-08-04T16:31:33.667Z
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Body: <p>I learned about adding entropy to RL algorithms through the notes provided in SpinningUp. They explained how entropy is added to the <a href="https://spinningup.openai.com/en/latest/algorithms/sac.html" rel="nofollow noreferrer">SAC</a> algorithm. Here is my understanding - In entropy regularized RL, one adds an entropy bonus <span class="math-container">$H$</span> to the reward function. This changes the objective function of RL such that one needs to find a policy that'll maximize the expected sum of rewards <em>and entropy</em>.</p>
  <p>In order to incorporate entropy into the actor (policy), one would have to maximize the q-function which contains the entropy. To incorporate entropy into the critic (value function), one would have to add entropy while computing the expected sum of future rewards. I hope this correct.</p>
  <p>Now, I am trying to understand how to add entropy to the PPO algorithm which also has an actor and a critic. I think it'll be trivial to add entropy to add the critic. I just need to add entropy to the target return function while computing the loss between the predicted value and target. I have no idea how to incorporate entropy to the policy function though.</p>
  
Tags: <reinforcement-learning><deep-rl><proximal-policy-optimization><entropy>
Title: How do I add Entropy to a PPO algorithm?
UserDisplayName: desert_ranger
ThreadText: # How do I add Entropy to a PPO algorithm?
  
  (Asked by: username_0 on 2023-08-03)
  
  I learned about adding entropy to RL algorithms through the notes provided in SpinningUp. They explained how entropy is added to the [SAC](https://spinningup.openai.com/en/latest/algorithms/sac.html) algorithm. Here is my understanding - In entropy regularized RL, one adds an entropy bonus $H$ to the reward function. This changes the objective function of RL such that one needs to find a policy that'll maximize the expected sum of rewards *and entropy*.
  
  In order to incorporate entropy into the actor (policy), one would have to maximize the q-function which contains the entropy. To incorporate entropy into the critic (value function), one would have to add entropy while computing the expected sum of future rewards. I hope this correct.
  
  Now, I am trying to understand how to add entropy to the PPO algorithm which also has an actor and a critic. I think it'll be trivial to add entropy to add the critic. I just need to add entropy to the target return function while computing the loss between the predicted value and target. I have no idea how to incorporate entropy to the policy function though.
  
  **Tags:** reinforcement-learning, deep-rl, proximal-policy-optimization, entropy
  
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  ## 1 answers
  
  ### Answer by username_1 on 2023-08-04. Score: 1 | Accepted answer
  
  In the regular PPO formulation, which I don't believe is strictly following maximum-entropy RL, only the actor has the entropy term which is employed as a regularizer to favor exploration. (I guess adding $H$ to both actor and critic in PPO would result in a different agent.)
  
  Basically, since the actor parameterizes a probability distribution like a Gaussian, whose entropy has an analytical form, you need to just compute that and add a term (weighted by a coefficient) to the loss function. If you don't know how to compute $H$, you can estimate it by taking the negative log-probability, averaging over the batch: $H[\pi]\approx -\frac1B\sum\_{a,s\in B}-\log \pi(a\mid s)$. Note: the first minus is to ensure the optimizer to maximize the entropy.
  
  
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