README.md

---
language:
- en
tags:
- code
pretty_name: eDriveMORL
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: mpc_trajectories
    path:
    - minari_export/minari_MPC/data/dataset.json
  - split: rule_trajectories
    path:
    - minari_export/minari_Rule/data/dataset.json
---
# eDriveMORL: Offline Reinforcement Learning Dataset and Benchmark for FCEVs

**eDriveMORL** is a benchmark suite for offline reinforcement learning on fuel cell electric vehicle (FCEV) systems. It includes: 

- High-fidelity FCEV dynamic simulation
- Minari-compatible offline datasets
- Multiple D3RLpy-compatible algorithm configs
- Custom reward function and thermal modeling


## 📦 Project Structure

```
.
├── run.py                      # Run benchmark for all algorithms via CLI
├── train.py                    # Generate offline dataset from Minari
├── register_minari_dataset.py  # Register Minari-compatible FCEV dataset
├── datasets/                   # Stores generated D3RLpy HDF5 datasets
├── requirements.txt            # Python dependency list
├── fcev/                       # Core model implementation
└── README.md                   # You are here
```


## ⚙️ Environment Setup

0. **Git Download the code and dataset**

```bash
git lfs install
git clone git@hf.co:datasets/TJIET/eDriveMORL
```

1. **Create a Conda environment** (Python 3.9 recommended):

```bash
conda create -n fcev-benchmark python=3.9
conda activate fcev-benchmark
```

2. **Install required dependencies**:

```bash
cd eDriveMORL
pip install -r requirements.txt
```


## 🗂️ Step-by-Step Usage

### 1️⃣ Register the Minari Dataset

Before any training or dataset generation, register the Minari dataset:

```bash
python register_minari_dataset.py
```

This ensures that your local offline dataset (e.g., collected via MPC) is discoverable by `minari.load_dataset()`.



### 2️⃣ (Optional) Regenerate Offline Dataset

If you want to regenerate a D3RLpy-compatible dataset (HDF5 format), modify and run:

```bash
python train.py
```

This will create a `.h5` dataset under the `datasets/` folder, such as:

```
datasets/fcev-mpc-v1.h5
```

You can switch to different reward shaping or normalization settings inside `train.py`.


### 3️⃣ Run Offline RL Benchmarks

Run the benchmark suite using different algorithms (TD3+BC, CQL, AWAC, etc.):

```bash
python run.py \
  --algo CQL \
  --dataset-path datasets/fcev-mpc-v1.h5 \
  --drive-cycle CLTC-P-PartI.csv \
  --n-steps 10000 \
  --wandb-project fcev-offline-benchmark
```

🔧 Available algorithms:

- TD3PlusBC
- IQL
- CQL
- BCQ
- CalQL
- AWAC
- ReBRAC
- TACR
- PLAS
- PRDC
- BEAR

Use `--wandb` to enable logging to Weights & Biases.


## 📊 Dataset: `eDriveMORL`

All offline training is based on the **eDriveMORL** dataset, registered through Minari. It captures state-action-reward sequences collected via expert controllers (e.g., MPC) from a simulated FCEV model.

Dataset fields include:
- State: `[SOC, T_fc, T_core, T_surf, speed, acc]`
- Action: `[fc_ratio, cooling_level, coolant_split_ratio]`
- Reward: Custom function reflecting energy and thermal efficiency
- Termination: Episode end or infeasibility


## 📈 Logging & Evaluation

- 📝 TensorBoard logs saved under `tensorboard_logs/{algo}`
- 📁 File logs (e.g., model snapshots) under `d3rlpy_logs/{algo}`
- 📊 WandB metrics (optional): view your experiment dashboard online.