---
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.