How to explain and visualize a Q Learning Agent? [on hold]












-1















What are some common approaches and useful resources that will aid in explaining the behavior of a Q-Learning agent and visualizing Q values?



Here is an excerpt of some example Q values serialized to json:



[

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 9.7180743908492411E-05, 0.0, 6.0134871150517619E-05, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 2.7866205412015394E-05, 0.0, -3.5352503282357707E-05, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 0.002179680102508753, 0.0, 0.0003821282886147801, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],

  [ 0.0, 0.00044976255425384565, 0.0, 2.6171104054710165E-05, 0.0 ],

  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],





machine-learning reinforcement-learning accord.net q-learning







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put on hold as too broad by desertnaut, Jose Ricardo Bustos M., Bob Dalgleish, DebanjanB, Psi 5 hours ago


Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.




















    -1















    What are some common approaches and useful resources that will aid in explaining the behavior of a Q-Learning agent and visualizing Q values?



    Here is an excerpt of some example Q values serialized to json:



    [
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 9.7180743908492411E-05, 0.0, 6.0134871150517619E-05, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 2.7866205412015394E-05, 0.0, -3.5352503282357707E-05, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 0.002179680102508753, 0.0, 0.0003821282886147801, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
    
  [ 0.0, 0.00044976255425384565, 0.0, 2.6171104054710165E-05, 0.0 ],
    
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],





    machine-learning reinforcement-learning accord.net q-learning







    share|improve this question













    put on hold as too broad by desertnaut, Jose Ricardo Bustos M., Bob Dalgleish, DebanjanB, Psi 5 hours ago


    Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.


















      -1












      -1








      -1








      What are some common approaches and useful resources that will aid in explaining the behavior of a Q-Learning agent and visualizing Q values?



      Here is an excerpt of some example Q values serialized to json:



      [
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 9.7180743908492411E-05, 0.0, 6.0134871150517619E-05, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 2.7866205412015394E-05, 0.0, -3.5352503282357707E-05, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.002179680102508753, 0.0, 0.0003821282886147801, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.00044976255425384565, 0.0, 2.6171104054710165E-05, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],





      machine-learning reinforcement-learning accord.net q-learning







      share|improve this question














      What are some common approaches and useful resources that will aid in explaining the behavior of a Q-Learning agent and visualizing Q values?



      Here is an excerpt of some example Q values serialized to json:



      [
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 9.7180743908492411E-05, 0.0, 6.0134871150517619E-05, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 2.7866205412015394E-05, 0.0, -3.5352503282357707E-05, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.002179680102508753, 0.0, 0.0003821282886147801, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
      
  [ 0.0, 0.00044976255425384565, 0.0, 2.6171104054710165E-05, 0.0 ],
      
  [ 0.0, 0.0, 0.0, 0.0, 0.0 ],





      machine-learning reinforcement-learning accord.net q-learning




      machine-learning reinforcement-learning accord.net q-learning






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 19 hours ago









      GracieGracie

      1921212




      1921212




      put on hold as too broad by desertnaut, Jose Ricardo Bustos M., Bob Dalgleish, DebanjanB, Psi 5 hours ago


      Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.






      put on hold as too broad by desertnaut, Jose Ricardo Bustos M., Bob Dalgleish, DebanjanB, Psi 5 hours ago


      Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.


























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