Answers.Body: <p>Indeed DPG is essentially a policy-based method because it directly optimizes the policy actor by taking the gradient of the expected return w.r.t. its parameters and also is deterministic as opposed to sampling from a probability distribution. DPG also includes a critic typically implemented as a Q-function approximator which is essential because it enables the actor to compute gradients through the Q-function allowing it to improve by following the deterministic policy gradient. Since the critic here reduces variance in policy updates making learning more sample-efficient than pure policy-based methods that rely only on rewards from the environment, DPG belongs to the actor-critic method sub-category of the policy based method category following your classification scheme.</p>
  
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Body: <p>I have a question about whether the Deterministic Policy Gradient algorithm in it's basic form is policy-based or actor-critic. I have been searching for the answer for a while and in some cases it says it's policy-based (like in a post here a while ago), whereas in others it does not explicitly says it's an actor-critic, but that it uses an actor-critic framework to optmize the policy.</p>
  <p>I know that actor-critic methods are essentially policy-based methods augmented with a critic to improve learning efficiency and stability.</p>
  <p>I'm asking this question as well because I'm trying to make a classification of RL algorithms for a project of my own. I attach the table, you can point out any mistakes I might have made or things that could be improved:</p>
  <p><a href="https://i.sstatic.net/UDaS34wE.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/UDaS34wE.png" alt="enter image description here" /></a></p>
  
Tags: <reinforcement-learning><actor-critic-methods><algorithm-request><deterministic-policy><deterministic-pg-theorem>
Title: Is DPG a policy-based method or an actor-critic method?
UserDisplayName: marc_spector
ThreadText: # Is DPG a policy-based method or an actor-critic method?
  
  (Asked by: username_0 on 2024-11-12)
  
  I have a question about whether the Deterministic Policy Gradient algorithm in it's basic form is policy-based or actor-critic. I have been searching for the answer for a while and in some cases it says it's policy-based (like in a post here a while ago), whereas in others it does not explicitly says it's an actor-critic, but that it uses an actor-critic framework to optmize the policy.
  
  I know that actor-critic methods are essentially policy-based methods augmented with a critic to improve learning efficiency and stability.
  
  I'm asking this question as well because I'm trying to make a classification of RL algorithms for a project of my own. I attach the table, you can point out any mistakes I might have made or things that could be improved:
  
  [![enter image description here](https://i.sstatic.net/UDaS34wE.png)](https://i.sstatic.net/UDaS34wE.png)
  
  **Tags:** reinforcement-learning, actor-critic-methods, algorithm-request, deterministic-policy, deterministic-pg-theorem
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-11-12. Score: 2 | Accepted answer
  
  Indeed DPG is essentially a policy-based method because it directly optimizes the policy actor by taking the gradient of the expected return w.r.t. its parameters and also is deterministic as opposed to sampling from a probability distribution. DPG also includes a critic typically implemented as a Q-function approximator which is essential because it enables the actor to compute gradients through the Q-function allowing it to improve by following the deterministic policy gradient. Since the critic here reduces variance in policy updates making learning more sample-efficient than pure policy-based methods that rely only on rewards from the environment, DPG belongs to the actor-critic method sub-category of the policy based method category following your classification scheme.
  
  
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