Row 3991

Row ID: 3991 | Dataset Entry | Axioma AXP Content Repository

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I'm currently developing a game and I'm using a neural net to create an AI opponent for players to play against. The game has a structure that is comparable to board games like chess and go, although it is significantly more complicated. I have a 'tile' class that has a 'state' sub-object, the state determines the behavior of the tile. The full game board consists of 98 tiles (7x14). I am still working on this aspect but when it is complete there will be around 200 or so state types (currently I am using a simplified prototype in order more quickly test the functionality of the neural net). I initially was giving a bool feature for each state, so for each input there would be a single state-feature with value 1.0 and all others being 0.0. Of course, it seems to me that it would quickly become impractical once I begin training with the real product and not the simplistic prototype. But I'm certain that if I simply put the state as a singular float input with the index number of the state as the value, the network would have great difficulty deciphering any meaning . This would lead to far slower training speed and most likely it would also plateau at a lower level. Obviously tokenization is a potential solution. I've looked into the PyTorch tokenizer and it seems that it is designed specifically for natural language. Is there a way to use the tokenizer for types or there a better method that I could use?

FieldValue
text I'm currently developing a game and I'm using a neural net to create an AI opponent for players to play against. The game has a structure that is comparable to board games like chess and go, although it is significantly more complicated. I have a 'tile' class that has a 'state' sub-object, the state determines the behavior of the tile. The full game board consists of 98 tiles (7x14). I am still working on this aspect but when it is complete there will be around 200 or so state types (currently I…
label r/pytorch
dataType post
communityName r/pytorch
datetime 2024-03-31
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Raw Record

{
  "text": "I'm currently developing a game and I'm using a neural net to create an AI opponent for players to play against. The game has a structure that is comparable to board games like chess and go, although it is significantly more complicated. I have a 'tile' class that has a 'state' sub-object, the state determines the behavior of the tile. The full game board consists of 98 tiles (7x14). I am still working on this aspect but when it is complete there will be around 200 or so state types (currently I am using a simplified prototype in order more quickly test the functionality of the neural net). I initially was giving a bool feature for each state, so for each input there would be a single state-feature with value 1.0 and all others being 0.0. Of course, it seems to me that it would quickly become impractical once I begin training with the real product and not the simplistic prototype. But I'm certain that if I simply put the state as a singular float input with the index number of the state as the value, the network would have great difficulty deciphering any meaning . This would lead to far slower training speed and most likely it would also plateau at a lower level. Obviously tokenization is a potential solution. I've looked into the PyTorch tokenizer and it seems that it is designed specifically for natural language. Is there a way to use the tokenizer for types or there a better method that I could use?",
  "label": "r/pytorch",
  "dataType": "post",
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  "datetime": "2024-03-31",
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}
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