Answers.Body: <p>Backpropagation is a subroutine often used when training Artificial Neural Networks with a Gradient Descent learning algorithm. Gradient Descent requires the computation of the error gradient, i.e. derivatives, of a cost function with respect to the network parameters. BP allows you to find this gradient a lot faster than using naive methods.</p>
  <p>Reinforcement Learning refers to inferring &quot;optimal&quot; behavior, i.e. a strategy, of an agent maximizing some goal in an environment. Depending on the specific RL algorithm, BP may be employed to adjust the parameters of a function approximator used to represent aspects of the environment and or the agent.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-01-14T00:39:40.317Z
Answers.LastActivityDate: 2021-12-20T23:12:28.823Z
Answers.LastEditDate: 2021-12-20T23:12:28.823Z
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Body: <p>What is the relation between back-propagation and reinforcement learning?</p>
  
Tags: <reinforcement-learning><comparison><backpropagation>
Title: What is the relation between back-propagation and reinforcement learning?
UserDisplayName: Sourav Dalai
ThreadText: # What is the relation between back-propagation and reinforcement learning?
  
  (Asked by: username_0 on 2018-01-13)
  
  What is the relation between back-propagation and reinforcement learning?
  
  **Tags:** reinforcement-learning, comparison, backpropagation
  
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  ## 1 answers
  
  ### Answer by username_1 on 2018-01-14. Score: 6
  
  Backpropagation is a subroutine often used when training Artificial Neural Networks with a Gradient Descent learning algorithm. Gradient Descent requires the computation of the error gradient, i.e. derivatives, of a cost function with respect to the network parameters. BP allows you to find this gradient a lot faster than using naive methods.
  
  Reinforcement Learning refers to inferring "optimal" behavior, i.e. a strategy, of an agent maximizing some goal in an environment. Depending on the specific RL algorithm, BP may be employed to adjust the parameters of a function approximator used to represent aspects of the environment and or the agent.
  
  
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