Body: <p>I have recently just completed a course on deep learning and I feel like an intermediate, but I still don't know how to structure this problem.</p>
  <p>I'm looking to create a NN to play the card game <a href="https://splinterlands.fandom.com/wiki/Splinterlands_Wiki" rel="nofollow noreferrer">Splinterlands</a>.</p>
  <p>I have the history of battles, the cards played, who won, and the battle rules and constraints.</p>
  <p>I'm struggling with how best to model the inputs. I think that standard feature engineering and the encoding of all the variables affect the game, like manor, cards chosen, rules, etc.</p>
  <p>How best to constrain the outputs? For example, the model needs to select cards I have available, etc., while the history contains cards I don't.</p>
  <p>I know it's a long shot, but just looking for some inspiration :)</p>
  <p>The basic rules of splinterlands are as follows:</p>
  <ol>
  <li>Start battle (you are given mana and set of rules, like no
  shields etc)</li>
  <li>Then select a summon (some rules say it has to be
  of certain, you also want to select your highest level summoner)</li>
  <li>Then select 6 monsters ( each monster has a position and
  stats like attack, speed, health and mana cost)</li>
  <li>Press battle</li>
  </ol>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-17T13:31:08.930Z
Comments.Text: Can you summarize the rules of Splinterlands? Most of us have not played it.
Comments.UserDisplayName: Criticize SE actions means ban
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-18T05:25:44.230Z
Comments.Text: you need to use a reinforcement learning approach.
Comments.UserDisplayName: Taw
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-23T16:15:48.623Z
Comments.Text: hey i have edited and added some rules.. i think it helps paint a picture :)
Comments.UserDisplayName: Chris
Tags: <neural-networks><card-games>
Title: How to model the inputs and outputs of the neural network for the Splinterlands card game?
UserDisplayName: Chris
ThreadText: # How to model the inputs and outputs of the neural network for the Splinterlands card game?
  
  (Asked by: username_0 on 2021-09-16)
  
  I have recently just completed a course on deep learning and I feel like an intermediate, but I still don't know how to structure this problem.
  
  I'm looking to create a NN to play the card game [Splinterlands](https://splinterlands.fandom.com/wiki/Splinterlands_Wiki).
  
  I have the history of battles, the cards played, who won, and the battle rules and constraints.
  
  I'm struggling with how best to model the inputs. I think that standard feature engineering and the encoding of all the variables affect the game, like manor, cards chosen, rules, etc.
  
  How best to constrain the outputs? For example, the model needs to select cards I have available, etc., while the history contains cards I don't.
  
  I know it's a long shot, but just looking for some inspiration :)
  
  The basic rules of splinterlands are as follows:
  
  1. Start battle (you are given mana and set of rules, like no
     shields etc)
  2. Then select a summon (some rules say it has to be
     of certain, you also want to select your highest level summoner)
  3. Then select 6 monsters ( each monster has a position and
     stats like attack, speed, health and mana cost)
  4. Press battle
  
  **Tags:** neural-networks, card-games
  
  > **Comments:**
  > **username_1**: Can you summarize the rules of Splinterlands? Most of us have not played it.  
  > **username_2**: you need to use a reinforcement learning approach.  
  > **username_0**: hey i have edited and added some rules.. i think it helps paint a picture :)  
  
  ---
  
  ## 0 answers
  
  