Lux AI 34th Place Solution

My 34th place solution in Lux AI competition @kaggle
https://www.kaggle.com/c/lux-ai-2021

Transcript

1. Lux AI Challenge
   Copyright 2021 @kuto_bopro
   Meta Kaggle
   Collection of episodes
   ・Team: Toad Brigade
   ・LB score > 1900
   ・only win game
   ・about 1000 episodes(3 submissions)
   Unet Imitation Learning approach inspired by nosound(@zharch)
   obs
   horizontal flip vertical flip random roll(-5~5)
   TTA
   obs obs
   Global features
   (8ch,4,4)
   Observation map
   (17ch, 32, 32)
   Policy map
   (3ch,32,32)
   ・Units counts (×2)
   ・Citytiles counts (×2)
   ・Research points (×2)
   ・turn / cycle
   Data Sampling
   ・Random sampling up to 4 units
   actions in each turn
   ・Downsampling center actions
   Extract units policy
   from each units position
   Image reference: https://www.lux-ai.org/
   ・Units position/cooldown/resource (×2)
   ・Citytiles position/cooldown/fuel-lightupkeep ratio (×2)
   ・Wood/Coal/Uranium positions
   ・Road level
   ・Effective map area
   Create 8 pattern policy maps and apply mean
   UNet model
   Decide citytile actions
   by simple rule
   Create 4 batch by rotation
   input
   (4 batches)
   Policy maps
   (4batch, 3ch, 32, 32) Final policy map
   (6ch, 32, 32)
   Hierarchize move actions (shared by nosound)
   output 3ch
   policy map
   (4 batches)
   90°
   180°
   270°
   90°
   180°
   270°
   0ch: Center Action → batch mean
   1ch: Move Action
   1st batch: north
   2nd batch: west
   3rd batch: south
   4th batch: east
   2ch: Build City Action → batch mean
   kuto(@kuto0633)
   Final policy map
   (6ch,32,32)
   Observation maps(4batch, 17ch, 32,32)
   0ch: Move Center
   1ch: Move North
   2ch: Move West
   3ch: Move South
   4ch: Move East
   5ch: Build City
   Calculate 4 move actions
   as one direction
   State Value
   (for RL and MCTS but not work)
   16 64 64
   128 128
   256 256
   256 256 8
   256 256 +8 256 256
   128 + 256 128 128
   64 + 128 64 64 3
   32×32
   32×32
   32×32
   16×16
   16×16
   16×16
   8×8
   8×8
   8×8
   4×4
   4×4
   4×4
   32×32
   32×32
   32×32
   16×16
   16×16
   8×8
   8×8
   FC
   BN
   ReLU
   FC
   264→64 64→1
   Conv2d BatchNorm2d, ReLU
   MaxPooling2d
   Upsample
   Concatenate
   Private LB: 34th (score 1570)
   

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