Datasets:

polixirai
/

NeoRL2

---
license: apache-2.0
task_categories:
- reinforcement-learning
language:
- en
tags:
- offlinerl
pretty_name: neorl
size_categories:
- 100M<n<1B
configs:
- config_name: DMSD
  data_files:
  - split: train
    path: "DMSD/train/*.parquet"
  - split: val
    path: "DMSD/val/*.parquet"
- config_name: Fusion
  data_files:
  - split: train
    path: "Fusion/train/*.parquet"
  - split: val
    path: "Fusion/val/*.parquet"
- config_name: Pipeline
  data_files:
  - split: train
    path: "Pipeline/train/*.parquet"
  - split: val
    path: "Pipeline/val/*.parquet"
- config_name: RandomFrictionHopper
  data_files:
  - split: train
    path: "RandomFrictionHopper/train/*.parquet"
  - split: val
    path: "RandomFrictionHopper/val/*.parquet"
- config_name: RocketRecovery
  data_files:
  - split: train
    path: "RocketRecovery/train/*.parquet"
  - split: val
    path: "RocketRecovery/val/*.parquet"
- config_name: SafetyHalfCheetah
  data_files:
  - split: train
    path: "SafetyHalfCheetah/train/*.parquet"
  - split: val
    path: "SafetyHalfCheetah/val/*.parquet"
- config_name: Salespromotion
  data_files:
  - split: train
    path: "Salespromotion/train/*.parquet"
  - split: val
    path: "Salespromotion/val/*.parquet"
- config_name: Simglucose
  data_files:
  - split: train
    path: "Simglucose/train/*.parquet"
  - split: val
    path: "Simglucose/val/*.parquet"
- config_name: Simglucose-high
  data_files:
  - split: train
    path: "Simglucose-high/train/*.parquet"
  - split: val
    path: "Simglucose-high/val/*.parquet"
---
# 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) 

| 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).