Difference between revisions of "LightR"
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:: Directly optimizing prior probability of getting hit (max escape angle, distancing and multi-wave risk fusion) | :: Directly optimizing prior probability of getting hit (max escape angle, distancing and multi-wave risk fusion) | ||
:: Per-instance level optimization of the above (Pareto frontier) | :: Per-instance level optimization of the above (Pareto frontier) | ||
| + | :: List-wise modeling of targeting & surfing | ||
__NOTOC__ __NOEDITSECTION__ | __NOTOC__ __NOEDITSECTION__ | ||
{{Template:Bot Categorizer|author=Xor|isMega=true|isOneOnOne=true|isMelee=false|isOpenSource=false|extends=Interface}} | {{Template:Bot Categorizer|author=Xor|isMega=true|isOneOnOne=true|isMelee=false|isOpenSource=false|extends=Interface}} | ||
Revision as of 11:49, 20 March 2022
- LightR Sub-pages:
- Version History
This page is under construction. For recent activities, see Version History.
- Design principle
- Strategy light, machine learning heavy
- Central goal
- Learned models -> learned systems
- Planned experiments
- Towards deep learning:
- Multiple hand-tuned danger models -> Expert model & gate model
- Hand-crafted features with naive KNN -> Search-based sequence model
- Offline pre-training & online fine-tuning of everything above.
- Towards differentiable programming:
- Directly optimizing prior probability of getting hit (max escape angle, distancing and multi-wave risk fusion)
- Per-instance level optimization of the above (Pareto frontier)
- List-wise modeling of targeting & surfing
