Body: <p>There are different ways to set the reward function, such as extrinsic (externally provided rewards), <a href="https://link.springer.com/chapter/10.1007/978-3-642-32375-1_2" rel="nofollow noreferrer">intrinsic</a> (the rewards are generated by the agents themselves based on their internal state and their own model of the environment), and shaped rewards (combination of both intrinsic and extrinsic). However, I have seen several examples where the reward quantification can be [-1,1] or [0,1] or [-1,0], or even [-1, much greater than 1]. Is there any criteria for quantifying the reward, or is it just arbitrary?</p>
  <p>How does this selection of the quantification of rewards affect the learning? For example, in <a href="https://gamedevacademy.org/unity-machine-learning-training-tutorial/" rel="nofollow noreferrer">this</a>, an agent that learns how to push a block to a random location in a 3D environment, the author sets two rules for the rewards. I) +5 if the agent successfully pushes the block to the goal. II) -1/MaxSteps for every action (this encourages the agent to use fewer actions). I understand that the agent will never get a total reward of 5 at the end of an episode. Hence, in this case, how can we determine the reward threshold in order to consider that the agent mastered this problem? Is it also arbitrary?</p>
  <p>I'd appreciate it if you could help me to clarify this. Or if you know any article/book related where I can understand it.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-01-16T04:28:53.313Z
Comments.Text: Thanks for the book, I will read it right away
Comments.UserDisplayName: John Prada
Tags: <reinforcement-learning><reward-functions><reward-design>
Title: In RL, is the quantification of the reward function arbitrary? Does it affect the learning?
UserDisplayName: John Prada
ThreadText: # In RL, is the quantification of the reward function arbitrary? Does it affect the learning?
  
  (Asked by: username_0 on 2023-01-09)
  
  There are different ways to set the reward function, such as extrinsic (externally provided rewards), [intrinsic](https://link.springer.com/chapter/10.1007/978-3-642-32375-1_2) (the rewards are generated by the agents themselves based on their internal state and their own model of the environment), and shaped rewards (combination of both intrinsic and extrinsic). However, I have seen several examples where the reward quantification can be [-1,1] or [0,1] or [-1,0], or even [-1, much greater than 1]. Is there any criteria for quantifying the reward, or is it just arbitrary?
  
  How does this selection of the quantification of rewards affect the learning? For example, in [this](https://gamedevacademy.org/unity-machine-learning-training-tutorial/), an agent that learns how to push a block to a random location in a 3D environment, the author sets two rules for the rewards. I) +5 if the agent successfully pushes the block to the goal. II) -1/MaxSteps for every action (this encourages the agent to use fewer actions). I understand that the agent will never get a total reward of 5 at the end of an episode. Hence, in this case, how can we determine the reward threshold in order to consider that the agent mastered this problem? Is it also arbitrary?
  
  I'd appreciate it if you could help me to clarify this. Or if you know any article/book related where I can understand it.
  
  **Tags:** reinforcement-learning, reward-functions, reward-design
  > **Comments:**
  > **username_0**: Thanks for the book, I will read it right away  
  
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  ## 0 answers
  
  