Update README.md

README.md

@@ -65,4 +65,120 @@ configs:
   - split: val
     path: "Simglucose-high/val/*.parquet"
 ---
-This is the dataset for NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios
+# Dataset Card for NeoRL‑2: Near Real‑World Benchmarks for Offline Reinforcement Learning
+
+## Dataset Summary
+
+**NeoRL-2** is a collection of seven near–real-world offline-RL datasets *plus* their evaluation simulators. This repo we provide the offline-RL dataset, while the simulators are in <https://github.com/polixir/NeoRL2>.
+
+Each task injects one or more realistic challenges—delays, exogenous disturbances, global safety constraints, traditional rule-based data, and/or severe data scarcity—into a lightweight control environment.
+
+
+---
+
+## Dataset Details
+
+| Challenge | Brief description | Appears in |
+|-----------|-------------------|------------|
+| **Delay** | Long & variable observation-to-effect latency | Pipeline, Simglucose |
+| **External factors** | State variables the agent cannot influence (e.g. wind, ground-friction) | RocketRecovery, RandomFrictionHopper, Simglucose |
+| **Global safety constraints** | Hard limits that must never be violated | SafetyHalfCheetah |
+| **Rule-based behaviour policy** | Trajectories from a PID or other deterministic controller | DMSD |
+| **Severely limited data** | Tiny datasets reflecting expensive experimentation | Fusion, RocketRecovery, SafetyHalfCheetah |
+
+* **Curated by:** Polixir Technologies  
+* **Paper:** Gao *et al.* “NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios”, arXiv:2503.19267 (2025)  
+* **Repository (the environments for the datasets are in here):** <https://github.com/polixir/NeoRL2>  
+* **Task:** offline / batch reinforcement learning
+
+## Uses
+
+### Direct Use
+* Benchmarking offline-RL algorithms under near-deployment conditions  
+* Studying robustness to delays, safety limits, exogenous disturbances and data scarcity  
+* Developing data-efficient model-based or model-free methods able to outperform conservative behaviour policies  
+
+#### Loading example
+```python
+from datasets import load_dataset
+
+dmsd = load_dataset("polixir/neorl2", "DMSD", split="train")
+state, action, reward, next_state, done = dmsd[0].values()
+```
+
+### Out-of-Scope Use
+* Online RL with unlimited interaction  
+* Safety-critical decision-making without extensive validation on the real system  
+
+
+---
+
+## Dataset Structure
+
+Each Parquet row contains  
+
+| Key                | Type        | Description                                     |
+|--------------------|-------------|-------------------------------------------------|
+| `observations`     | float32[]   | Raw observation vector (dim varies per task)    |
+| `actions`          | float32[]   | Continuous action taken by the behaviour policy |
+| `rewards`          | float32     | Scalar reward                                   |
+| `next_observations`| float32[]   | Observation at the next timestep                |
+| `terminals`        | bool        | `True` if episode ended (termination or safety) |
+
+Typical dataset sizes are **≈100 k transitions**; *Fusion*, *RocketRecovery* and *SafetyHalfCheetah* are smaller by design.
+
+---
+
+## Baseline Benchmark
+
+### Normalised return (0 – 100) – best of 3 seeds
+
+| Task | Data | BC | CQL | EDAC | MCQ | TD3BC | MOPO | COMBO | RAMBO | MOBILE |
+|------|------|----|----|------|----|------|------|------|------|-------|
+| **Pipeline** | 69.25 | 68.6 ± 13.4 | **81.1 ± 8.3** | 72.9 ± 4.6 | 49.7 ± 7.4 | **81.9 ± 7.5** | −26.3 ± 92.7 | 55.5 ± 4.3 | 24.1 ± 74.4 | 65.5 ± 4.1 |
+| **Simglucose** | 73.9 | **75.1 ± 0.7** | 11.0 ± 3.4 | 8.1 ± 0.3 | 29.6 ± 5.7 | **74.2 ± 0.4** | 34.6 ± 28.1 | 23.2 ± 2.5 | 10.8 ± 0.9 | 9.3 ± 0.2 |
+| **RocketRecovery** | 75.3 | 72.8 ± 2.5 | 74.3 ± 1.4 | 65.7 ± 9.8 | **76.5 ± 0.8** | **79.7 ± 0.9** | −27.7 ± 105.6 | 74.7 ± 0.7 | −44.2 ± 263.0 | 43.7 ± 17.5 |
+| **RandomFrictionHopper** | 28.7 | 28.0 ± 0.3 | 33.0 ± 1.2 | **34.7 ± 1.3** | 31.7 ± 1.3 | 29.5 ± 0.7 | 32.5 ± 5.8 | 34.1 ± 4.7 | 29.6 ± 7.2 | **35.1 ± 0.5** |
+| **DMSD** | 56.6 | 65.1 ± 1.6 | 70.2 ± 1.1 | **78.7 ± 2.3** | **77.8 ± 1.2** | 60.0 ± 0.8 | 68.2 ± 0.7 | 68.3 ± 0.4 | 76.2 ± 1.9 | 64.4 ± 0.8 |
+| **Fusion** | 48.8 | 55.2 ± 0.3 | 55.9 ± 1.9 | **58.0 ± 0.7** | 49.7 ± 1.1 | 54.6 ± 0.8 | −11.6 ± 22.2 | 55.5 ± 0.3 | **59.6 ± 5.0** | 5.0 ± 7.1 |
+| **SafetyHalfCheetah** | 73.6 | 70.2 ± 0.4 | 71.2 ± 0.6 | 53.1 ± 11.1 | 54.7 ± 4.3 | 68.6 ± 0.4 | 23.7 ± 24.3 | 57.8 ± 13.3 | −422.4 ± 307.5 | 8.7 ± 3.9 |
+
+### How often do algorithms beat the behaviour policy?
+
+| Margin | BC | CQL | EDAC | MCQ | TD3BC | MOPO | COMBO | RAMBO | MOBILE |
+|--------|----|----|----|----|------|------|------|------|-------|
+| ≥ 0    | 3 | 4 | 4 | 4 | **6** | 2 | 3 | 3 | 2 |
+| ≥ +3   | 2 | 4 | 4 | 2 | **4** | 2 | 3 | 2 | 2 |
+| ≥ +5   | 2 | 3 | 3 | 1 | **2** | 1 | 3 | 2 | 2 |
+| ≥ +10  | 0 | 2 | 1 | 1 | **1** | 1 | 1 | 2 | 0 |
+
+### Key conclusions
+
+* No baseline “solves” any task (score ≥ 95). Best result is TD3BC’s 81.9 on *Pipeline*.  
+* **TD3BC** is the most reliable algorithm, surpassing the data in 6 / 7 tasks and still leading at stricter margins.  
+* Model-based methods (MOPO, RAMBO, and MOBILE) are brittle, with large variance and occasional catastrophic divergence.  
+* *DMSD* is easiest: many algorithms exceed the behaviour policy by 20 + points thanks to simple PID data.  
+* *SafetyHalfCheetah* is hardest: every method trails the data due to strict safety penalties and limited samples.  
+* In general, model-free approaches show smaller error bars than model-based ones, underlining the challenge of learning accurate dynamics under delay, disturbance and scarcity.
+
+---
+
+## Citation
+
+```bibtex
+@misc{gao2025neorl2,
+  title   = {NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios},
+  author  = {Songyi Gao and Zuolin Tu and Rong-Jun Qin and Yi-Hao Sun and Xiong-Hui Chen and Yang Yu},
+  year    = {2025},
+  eprint  = {2503.19267},
+  archivePrefix = {arXiv},
+  primaryClass = {cs.LG}
+}
+```
+
+---
+
+## Contact
+
+Questions or bug reports? Please open an issue on the [NeoRL-2 GitHub repo](https://github.com/polixir/NeoRL2).
+