OpenAI Gym custom env. multi discrete action space or tuple space example? Announcing the...

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OpenAI Gym custom env. multi discrete action space or tuple space example?



Announcing the arrival of Valued Associate #679: Cesar Manara
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I am trying to implement my custom env. into the openai gym



So far, I am using continuous action space (6 ch)



so input dimension can be used with env.action_space.shape[0]



But I found that it is not stable in my env. I decided to move to multi discrete space like with limited interger values (100, 100, 10, 5, 6, 100 ) something like this



But when I was not able to find any example using multidiscrete or tuple space to handle my actions properly.



Anyone knows any example I can try for my custom env.?










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    0















    I am trying to implement my custom env. into the openai gym



    So far, I am using continuous action space (6 ch)



    so input dimension can be used with env.action_space.shape[0]



    But I found that it is not stable in my env. I decided to move to multi discrete space like with limited interger values (100, 100, 10, 5, 6, 100 ) something like this



    But when I was not able to find any example using multidiscrete or tuple space to handle my actions properly.



    Anyone knows any example I can try for my custom env.?










    share|improve this question







    New contributor




    Jake Jeong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.























      0












      0








      0








      I am trying to implement my custom env. into the openai gym



      So far, I am using continuous action space (6 ch)



      so input dimension can be used with env.action_space.shape[0]



      But I found that it is not stable in my env. I decided to move to multi discrete space like with limited interger values (100, 100, 10, 5, 6, 100 ) something like this



      But when I was not able to find any example using multidiscrete or tuple space to handle my actions properly.



      Anyone knows any example I can try for my custom env.?










      share|improve this question







      New contributor




      Jake Jeong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.












      I am trying to implement my custom env. into the openai gym



      So far, I am using continuous action space (6 ch)



      so input dimension can be used with env.action_space.shape[0]



      But I found that it is not stable in my env. I decided to move to multi discrete space like with limited interger values (100, 100, 10, 5, 6, 100 ) something like this



      But when I was not able to find any example using multidiscrete or tuple space to handle my actions properly.



      Anyone knows any example I can try for my custom env.?







      python






      share|improve this question







      New contributor




      Jake Jeong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







      New contributor




      Jake Jeong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question






      New contributor




      Jake Jeong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked 5 hours ago









      Jake JeongJake Jeong

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      New contributor




      Jake Jeong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      New contributor





      Jake Jeong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      Jake Jeong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






















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