Row 4098

Row ID: 4098 | Dataset Entry | Axioma AXP Content Repository

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I’m planning on building an fps bot that does not access any of the games memory, but uses image recognition. I know how to get the shooting and identifying enemies part down, but I am struggling on coming up with a solution for movement around the map.

I don’t think 3D spacial processing is a realistic solution due to how slow it would be. My current thought was to use the mini map and image match it to a number-gridded version of the entire map to grab a value for my position.

I was then going to learn how to train my model to predict the next direction to face based on my real gameplay from a similar position. I was going to do this one of two ways. One was sending screenshots of just my raw mini map with info on what keys I was pressing, what direction I facing, etc. and passing that to a CNN. The other was preprocessing the screenshot, matching it to the gridded map to get a numerical value representing what grid im in, then passing that plus all key and directional info to an RNN.

Is something like this reasonably possible and is this an even slightly intelligent way to go about achieving it? This is my first project of this size and first time using NN so I still have much I don’t know.

FieldValue
text I’m planning on building an fps bot that does not access any of the games memory, but uses image recognition. I know how to get the shooting and identifying enemies part down, but I am struggling on coming up with a solution for movement around the map. I don’t think 3D spacial processing is a realistic solution due to how slow it would be. My current thought was to use the mini map and image match it to a number-gridded version of the entire map to grab a value for my position. I was then go…
label r/neuralnetworks
dataType post
communityName r/neuralnetworks
datetime 2024-04-02
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Raw Record

{
  "text": "I’m planning on building an fps bot that does not access any of the games memory, but uses image recognition. I know how to get the shooting and identifying enemies part down, but I am struggling on coming up with a solution for movement around the map.\n\nI don’t think 3D spacial processing is a realistic solution due to how slow it would be. My current thought was to use the mini map and image match it to a number-gridded version of the entire map to grab a value for my position. \n\nI was then going to learn how to train my model to predict the next direction to face based on my real gameplay from a similar position. I was going to do this one of two ways. One was sending screenshots of just my raw mini map with info on what keys I was pressing, what direction I facing, etc. and passing that to a CNN. The other was preprocessing the screenshot, matching it to the gridded map to get a numerical value representing what grid im in, then passing that plus all key and directional info to an RNN. \n\nIs something like this reasonably possible and is this an even slightly intelligent way to go about achieving it? This is my first project of this size and first time using NN so I still have much I don’t know.",
  "label": "r/neuralnetworks",
  "dataType": "post",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-04-02",
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}
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