Row 4126

Row ID: 4126 | Dataset Entry | Axioma AXP Content Repository

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Why do you want to use a CNN? I understand the appeal and the learning perspective. Plus, with images, the agent will only have access to limited information, but this is going to be computationally expensive. The model will try to infer the game state based on the screenshot (e.g. enemies positions), but you already know that as the developer and could give that directly to the model.

Two possibilities: you either give the model handcrafted features and don't use CNNs at all (easier and faster training, faster inference), or you go through the CNN option.

That said, let's discuss the CNN approach:

* I'd give the model a very, very small screenshots, smaller than 128px in width at least. 64x64, if that works, would be really nice to start. * Remove _all_ the shaders, anything expensive, no textures (unless important) and lighting. * If you reduce the resolution, it may be difficult to distinguish objects from background, so I'd color-code everything (e.g. ground=shades of blue depending on height, enemies=red, allies=green, objects=purple, sky=white, walls=shades of pink depending on normal, etc.). * I'd give the UI information directly to the model (health, currently equipped weapon, bullets available, etc.) * Your model has no memory over time, but I am not sure if you have enough data to train a sequence-to-sequence model. * I'd use half-precision during training, and prune the model after training to improve the inference time. * Reinforcement learning after training would really improve the model, but that's another level of difficulty implementation-wise.

FieldValue
text Why do you want to use a CNN? I understand the appeal and the learning perspective. Plus, with images, the agent will only have access to limited information, but this is going to be computationally expensive. The model will try to infer the game state based on the screenshot (e.g. enemies positions), but you already know that as the developer and could give that directly to the model. Two possibilities: you either give the model handcrafted features and don't use CNNs at all (easier and faster…
label r/neuralnetworks
dataType comment
communityName r/neuralnetworks
datetime 2024-04-03
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Raw Record

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  "text": "Why do you want to use a CNN? I understand the appeal and the learning perspective. Plus, with images, the agent will only have access to limited information, but this is going to be computationally expensive. The model will try to infer the game state based on the screenshot (e.g. enemies positions), but you already know that as the developer and could give that directly to the model.\n\nTwo possibilities: you either give the model handcrafted features and don't use CNNs at all (easier and faster training, faster inference), or you go through the CNN option.\n\nThat said, let's discuss the CNN approach:\n\n* I'd give the model a very, very small screenshots, smaller than 128px in width at least. 64x64, if that works, would be really nice to start.\n* Remove _all_ the shaders, anything expensive, no textures (unless important) and lighting.\n* If you reduce the resolution, it may be difficult to distinguish objects from background, so I'd color-code everything (e.g. ground=shades of blue depending on height, enemies=red, allies=green, objects=purple, sky=white, walls=shades of pink depending on normal, etc.).\n* I'd give the UI information directly to the model (health, currently equipped weapon, bullets available, etc.)\n* Your model has no memory over time, but I am not sure if you have enough data to train a sequence-to-sequence model.\n* I'd use half-precision during training, and prune the model after training to improve the inference time.\n* Reinforcement learning after training would really improve the model, but that's another level of difficulty implementation-wise.",
  "label": "r/neuralnetworks",
  "dataType": "comment",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-04-03",
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