license: other
license_name: game-footage
license_details: >-
Recordings of publicly listed Royale TV replays from Clash Royale (Supercell).
Game content belongs to Supercell; this dataset is for research on imitation
learning.
task_categories:
- reinforcement-learning
- video-classification
tags:
- clash-royale
- replays
- imitation-learning
- decision-transformer
- game-ai
AlphaClash replay corpus
Screen recordings of Clash Royale matches from Royale TV, scraped from a phone at 4x replay speed, each with a JSON sidecar and the deck screenshot its decks were read from. Collected for the AlphaClash agent (https://github.com/bednarjosef/AlphaClash) — a behaviour-cloned decision transformer over these replays.
Layout
manifest.jsonl one row per replay (see below)
replays/shard-0000/<stem>.mp4 the recording, 4x, 720x1600, ~37 MB
replays/shard-0000/<stem>.json sidecar
replays/shard-0000/<stem>_deck.png deck screenshot (absent on six early replays)
Shards hold 500 replays each and never change once written; new replays
append to the last shard, then open the next. <stem> is
<YYYYMMDD>-<HHMMSS>_<16-bit fingerprint>.
Sidecar
arena (pool: arena<N> or ranked_<league>), decks.player0/player1
(eight {slot, card, conf, level} in register-slug vocabulary),
crowns [p0, p1] and winner (0/1) where read, crowns_source (card
when read off the Royale TV card, tail/<rule> when read off the retained
video tail, absent when null), speed_multiplier (4 — divide video
timestamps by it), speed_change_at_s, battle_start_s, battle_end_s,
duration_s, capture_fps, device_profile.
manifest.jsonl
stem, shard, arena, crowns, crowns_source, winner, duration_s, speed_multiplier, decks (slugs only), has_deck_png — enough to pick a
subset without listing thirty thousand files.