| --- |
| 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. |
| |
