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Update README with new file direction removing the old one #83

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5 changes: 4 additions & 1 deletion preprocessing/sports/SAR_data/soccer/README.md
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Expand Up @@ -14,6 +14,7 @@ You can find detailed documentation on supported data providers [here](https://o
For information on supported preprocessing methods, visit [this documentation](https://openstarlab.readthedocs.io/en/latest/Pre_Processing/Sports/SAR_data/Data_Format/Soccer/index.html). The available preprocessing methods are:

- State Action Reward (SAR) Format
- SAR-to-RL Dataset Conversion (SAR2RL)

## Examples
Here are some examples of how to download and preprocess data:
Expand All @@ -29,10 +30,12 @@ Here are some examples of how to download and preprocess data:
## SAR-to-RL Dataset Conversion (DQN / QMIX)
This section describes a SAR-to-RL dataset conversion step that formats SAR outputs (`events.jsonl`) into tensors used by
DQN and QMIX training. This is a preprocessing/data-format step, not a training algorithm.
The conversion script is `soccer_sar_to_rl_dataset.py`.

This produces a single shared multi-agent dataset with:
- `observation`: `(B, T, N, O)` (N=10 attackers)
- `action`: `(B, T, N)` (discrete action ids; default vocab size 16 with `PAD=15`)
- `reward`, `done`, `mask`: `(B, T)`
- `onball_mask`: `(B, T, N)` (for masking unavailable actions)

### Run via `SAR_data(...).preprocess_data()`
You can run SAR2RL through the same entry point as SAR