Body: <p>Let us consider the following problem.
  A student is developing a robot that can roll a die. The robot grips and releases the die with an arm that can also twist, and uses two cameras that can see the entire rolling area at once for feedback. The goal is to develop methods that can roll the die in a way that increases the chance of landing on a desired side of the die. Please consider the environment that is the robot, die, and rolling area. Is this environment:</p>
  <p>-Accessible? If so, why?</p>
  <p>-Deterministic? If so, why?</p>
  <p>-Episodic? If so, why?</p>
  <p>-Static? If so, why?</p>
  <p>-Discrete? If so, why?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-22T13:29:16.090Z
Comments.Text: Is this a homework question? Can you please provide the source of this problem? It looks like you're expecting us to solve your homework problems without you even trying to solve them. Can you please at least tell us what you think might be the answer and why?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-22T23:49:49.273Z
Comments.Text: I am not a student. I just found this exercise online and was trying to solve it as a researcher trying to digest RL. This is the link: https://www.cs.jhu.edu/~phi/ai/exam/exam-2015.pdf
Comments.UserDisplayName: DSPinfinity
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-23T01:17:58.207Z
Comments.Text: Thanks for the clarification. I don't know if "accessible" just means "fully observable" here. I'm also not sure what "static" really means here, maybe "stationary". But do you know the definitions of deterministic, episodic and discrete? These are known concepts in RL. I'd recommend you tell us what you know or not about these concepts.
Comments.UserDisplayName: nbro
Tags: <reinforcement-learning><markov-decision-process>
Title: Properties of an example environment
UserDisplayName: DSPinfinity
ThreadText: # Properties of an example environment
  
  (Asked by: username_0 on 2024-04-21)
  
  Let us consider the following problem.
  A student is developing a robot that can roll a die. The robot grips and releases the die with an arm that can also twist, and uses two cameras that can see the entire rolling area at once for feedback. The goal is to develop methods that can roll the die in a way that increases the chance of landing on a desired side of the die. Please consider the environment that is the robot, die, and rolling area. Is this environment:
  
  -Accessible? If so, why?
  
  -Deterministic? If so, why?
  
  -Episodic? If so, why?
  
  -Static? If so, why?
  
  -Discrete? If so, why?
  
  **Tags:** reinforcement-learning, markov-decision-process
  
  > **Comments:**
  > **username_1**: Is this a homework question? Can you please provide the source of this problem? It looks like you're expecting us to solve your homework problems without you even trying to solve them. Can you please at least tell us what you think might be the answer and why?  
  > **username_0**: I am not a student. I just found this exercise online and was trying to solve it as a researcher trying to digest RL. This is the link: https://www.cs.jhu.edu/~phi/ai/exam/exam-2015.pdf  
  > **username_1**: Thanks for the clarification. I don't know if "accessible" just means "fully observable" here. I'm also not sure what "static" really means here, maybe "stationary". But do you know the definitions of deterministic, episodic and discrete? These are known concepts in RL. I'd recommend you tell us what you know or not about these concepts.  
  
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  ## 0 answers
  
  