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---
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. |