Update README.md

README.md

@@ -70,10 +70,7 @@ docker run -it \
 ### Data generation
 Trajectory generation is executed through the rollout runner combined with a behavior policy.
 The framework is policy-based: any controller that maps  
-\[
-s_t \rightarrow a_t
-\]
-can be used to generate trajectories.
+
 
 use the data generation script along with rollout runner to generate sequential data. 
 
@@ -82,6 +79,52 @@ a particular building model then it will work excellent. This works a framework
 
 We have rollouts which you can use to generate specific building location data or building type or combine different envolop locations and weather and building type.
 
+```bash
+# Inside Docker container
+cd /workspace
+
+python trajectory_generator.py \
+    --manifest patched_reference_data_base/OfficeSmall/reference_database.json \
+    --output_dir dataset \
+    --behavior seasonal_reactive \
+    --time_freq 900 \
+```
+
+Optional multi-building combinations:
+
+```bash
+python trajectory_generator.py \
+    --manifest patched_reference_data_base/OfficeMedium/reference_database.json \
+    --combine_climates True \
+    --combine_envelopes True \
+    --output_dir dataset_large
+```
+
+Each episode is stored as compressed `.npz`:
+
+```
+dataset/
+ ├── OfficeSmall__Buffalo__standard__episode_001.npz
+ ├── OfficeSmall__Dubai__high_internal__episode_002.npz
+ └── metadata.json
+```
+
+Each file contains:
+
+```python
+{
+  "observations": np.ndarray(T, state_dim),
+  "actions": np.ndarray(T, action_dim),
+  "rewards": np.ndarray(T),
+  "terminals": np.ndarray(T),
+  "state_keys": list,
+  "action_keys": list,
+  "meta": dict
+}
+```
+
+Temporal resolution: 15 minutes  
+Episode length: 35040 timesteps (1 simulation year)
 ### Training Phase
 
 After you have generated data you can move on to the training phase which , for our experiments we generted more than 2300 sequential data combinations and resulted in more than 3 million trajectories.