Update README.md

README.md

@@ -155,4 +155,46 @@ During inference, we deploy Gen-HVAC as a stateless HTTP microservice that loads
 maintains a short autoregressive context window internally, and returns multi-zone heating/cooling setpoints per control step. 
 In our experiments, EnergyPlus/Sinergym executes inside the Docker container, while the inference service runs on the host/server (CPU/GPU),  so the simulator can stream observation vectors to POST /predict (payload: {step, obs, info}) and receive an action vector in the response, with POST /reset 
 used to clear policy history at episode boundaries. When enabled, the DHIL module queries a local Ollama endpoint and updates the comfort RTG target at a low frequency (e.g., every 4 steps).
+
+## Repository Structure
+
+
+```text
+Gen-HVAC:Controller/
+│
+├── data_generator/                       # Data generation pipeline (EnergyPlus/Sinergym rollouts)
+│   ├── rollout_runner.py                 # Runs rollouts for selected building-weather configs and logs outputs
+│   └── trajectory_generator.py           # Creates trajectory datasets from rollouts
+│
+├── evaluation/                           # Evaluation scripts
+│
+├── Inference_&_LLM/                      # Inference + LLM/DHIL (Digital Human-in-the-Loop) components
+│   ├── inference.py                      # Runs local inference (loads model + produces actions)
+│   ├── inference_server.py               # Server wrapper for inference (API-based deployment)
+│   ├── digital_human_in_the_loop.py      # DHIL logic
+│   └── llm_client/                       # LLM client utilities
+│       └── ...                        
+│
+├── Model/                                # Saved model checkpoints + configs
+│   ├── Model_V1/                     
+│   │   ├── last.pt                       # Model checkpoint
+│   │   ├── model_config.json             # Training/model parameters
+│   │   └── report.json                   
+│   ├── Model_V2/ ...               
+│   ├── Model_V3/ ...                   
+│
+├── training/                             # Training code (DT model, embeddings, losses, trainer)
+│   ├── data_loader.py                    # Loads trajectories, builds tokens/batches, normalization, RTG, etc.
+│   ├── embeddings.py                     # Feature encoders + token embeddings (zone/global/RTG encodings)
+│   ├── losses.py                         # Action loss + auxiliary losses (physics/value/etc. if enabled)
+│   └── training.py                       # Main training entry point (train loop, checkpoints, logging)
+│
+└── utilities/                            # Shared utilities used across data-gen/training/eval/inference
+    ├── comfort.py                        # Comfort metric helpers 
+    ├── data_generator.py                 # Shared dataset helpers / schema utilities
+    ├── policy.py                         # Policy wrappers (DT policy interface, action post-processing)
+    ├── rewards.py                      
+    ├── rollout.py                        # Rollout utilities (env stepping, logging, post-processing)
+    └── tables.py                         
+