Body: <p>I am wondering how can I find the appropriate reward value for each specific problem. I know this is a highly empirical process, but I am sure that the value is not set totally at random. I want to know what are the general guidelines and practices to find the appropriate reward value for any reinforcement learning problem.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-03T18:10:55.923Z
Comments.Text: When you say "reward value", you mean the **reward function** (i.e. the function that gives the reward value for every action in some state), right?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-04T16:42:02.157Z
Comments.Text: Yes, but I am mostly having doubts about the ''value" I should give for rewarded states, for example, the reward of a goal in the final state of an episodic task. like in a maze what should be the reward value of the goal, +1, +50 or +100 or any other number? how this is determined?
Comments.UserDisplayName: Saeid Ghafouri
Tags: <reinforcement-learning><ai-design><rewards>
Title: How can I find the appropriate reward value for my reinforcement learning problem?
UserDisplayName: Saeid Ghafouri
ThreadText: # How can I find the appropriate reward value for my reinforcement learning problem?
  
  (Asked by: username_0 on 2020-04-03)
  
  I am wondering how can I find the appropriate reward value for each specific problem. I know this is a highly empirical process, but I am sure that the value is not set totally at random. I want to know what are the general guidelines and practices to find the appropriate reward value for any reinforcement learning problem.
  
  **Tags:** reinforcement-learning, ai-design, rewards
  > **Comments:**
  > **username_1**: When you say "reward value", you mean the **reward function** (i.e. the function that gives the reward value for every action in some state), right?  
  > **username_0**: Yes, but I am mostly having doubts about the ''value" I should give for rewarded states, for example, the reward of a goal in the final state of an episodic task. like in a maze what should be the reward value of the goal, +1, +50 or +100 or any other number? how this is determined?  
  
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  ## 0 answers
  
  