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Cochon123/clash-royale-policy-bc-v4

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Model Card

Clash Royale policy BC v4

Behavior-cloning next-action policy trained on RoyaleAPI replays. Predicts deck-slot, placement (zone + XY), and timing from causal action history.

Summary

Split	Slot top-1	Slot top-3	Zone acc	XY MAE	Timing MAE
Test	53.1%	93.3%	42.9%	5287	1.54s

—Battles: 22,554 total (train 15,788 / val 3,383 / test 3,383)
—Train samples: 480,032 action prefixes
—Created: 2026-08-08

Architecture

—Causal transformer trunk (d_model=160, 2 layers)
—Card-conditioned slot head + threat features (from v3)
—Card-conditioned zone / XY placement heads
—Reaction-window upweighting for defense-style answers

Files

File	Description
`best_model.pt`	PyTorch checkpoint (weights + vocab + config)
`vocab.json`	Card vocabulary
`report.json`	Full training report
`training_stages.json`	Per-epoch curves

Usage

bash

pip install git+https://github.com/cochon123/clash-royale-ai.git
hf download Cochon123/clash-royale-policy-bc-v4 --local-dir models/policy_bc_v4

cr-replays predict-policy data/raw/SOME_BATTLE.json --model-dir models/policy_bc_v4
cr-replays phone-lab --policy-v4 models/policy_bc_v4

Limits

Offline metrics are strong on slot choice; placement XY and live tempo are still weak relative to humans. Treat live phone play as an evaluation harness, not a finished agent.