diff --git a/preprocessing/sports/SAR_data/soccer/README.md b/preprocessing/sports/SAR_data/soccer/README.md
index 241063a..1e65562 100644
--- a/preprocessing/sports/SAR_data/soccer/README.md
+++ b/preprocessing/sports/SAR_data/soccer/README.md
@@ -25,3 +25,14 @@ Here are some examples of how to download and preprocess data:
 - **StatsBomb and SkillCorner Data:**
   - [Read the Docs Example](https://openstarlab.readthedocs.io/en/latest/Pre_Processing/Sports/SAR_data/Example/Soccer/Example_2/contents.html)
   - [Example Config File](https://github.com/open-starlab/PreProcessing/blob/master/example/config/statsbomb_skillcorner/preprocessing_statsbomb_skillcorner2024.json)
+
+## 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)