<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>

CC BY-SA 4.0
2020-04-03T18:10:55.923Z
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?
nbro
CC BY-SA 4.0
2020-04-04T16:42:02.157Z
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?
Saeid Ghafouri
<reinforcement-learning><ai-design><rewards>
How can I find the appropriate reward value for my reinforcement learning problem?
Saeid Ghafouri
# 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

