R2LPL

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Official implementation of “Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving”.

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# Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving

Official implementation of **R2LPL**: **R**ollout-**R**etrieval **L**ifelong **P**olicy **L**earning for autonomous driving.

**Paper:** [Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving](https://arxiv.org/abs/2606.30537).

R2LPL studies how a learned driving policy can improve from its own closed-loop mistakes. Instead of only fine-tuning on expert logs, R2LPL rolls out the current policy, mines recoverable mistake-related states, retrieves feasible corrective targets, and preserves the resulting knowledge through lifelong policy learning.

## Highlights

- **Mistake-driven policy improvement:** closed-loop rollouts expose failures, risk, and model-expert conflicts that are hard to capture from fixed expert demonstrations alone.
- **Rollout-retrieval corrective supervision:** R2LPL filters recoverable policy-induced states and retrieves feasible target anchors, turning sparse failure evidence into compact supervised knowledge.
- **Lifelong policy learning:** newly retrieved corrections are learned together with replayed memory, improving the policy over multiple rollout-learning rounds while reducing forgetting.

## Main Results

Closed-loop results on nuPlan. NR and R denote non-reactive and reactive simulation protocols. **Bold** marks the best score and <ins>underline</ins> marks the second-best score among the main compared learning-based methods; `R2LPL-ROCL-10-best` is reported as an envelope reference.

| Method | Training paradigm | Val14 NR | Val14 R | Test14-hard NR | Test14-hard R | Test14-random NR | Test14-random R |
|---|---|---:|---:|---:|---:|---:|---:|
| Expert | Human log replay | 93.53 | 80.32 | 85.96 | 68.80 | 94.03 | 75.86 |
| UrbanDriver | Imitation learning | 68.57 | 64.11 | 50.40 | 49.95 | 51.83 | 67.15 |
| [PDM-Open](https://github.com/autonomousvision/tuplan_garage) | Imitation learning | 53.53 | 54.24 | 33.51 | 35.83 | 52.81 | 57.23 |
| [PlanTF](https://github.com/jchengai/planTF) | Imitation learning | 84.27 | 76.95 | 69.70 | 61.61 | 85.62 | 79.58 |
| [PLUTO](https://github.com/jchengai/pluto) | Imitation learning | 88.89 | 78.11 | 70.03 | 59.74 | 89.90 | 78.62 |
| [Diffusion Planner](https://github.com/ZhengYinan-AIR/Diffusion-Planner) | Generative IL | 89.87 | 82.80 | 75.99 | 69.22 | 89.19 | 82.93 |
| [Flow Planner](https://github.com/DiffusionAD/Flow-Planner) | Generative IL | 90.43 | 83.31 | 76.47 | 70.42 | 89.88 | 82.93 |
| DFP | Generative IL | 90.33 | 79.97 | 76.91 | 63.56 | 90.69 | 81.96 |
| DFP-FM | Generative IL | **92.68** | 81.30 | <ins>79.43</ins> | 67.94 | 90.62 | 83.59 |
| [Plan-R1](https://github.com/XiaolongTang23/Plan-R1) | IL + RL alignment | 88.98 | **87.69** | 77.45 | <ins>77.20</ins> | <ins>91.23</ins> | **90.04** |
| R2LPL-base | Imitation learning | 75.39 | 73.87 | 60.67 | 65.25 | 70.74 | 72.96 |
| **R2LPL-ROCL-5** | **IL + R2LPL** | <ins>91.26</ins> | <ins>85.38</ins> | **83.51** | **78.38** | **92.25** | <ins>87.99</ins> |
| R2LPL-ROCL-10-best | IL + R2LPL envelope | 92.22 | 85.83 | 86.54 | 78.88 | 93.94 | 88.20 |

R2LPL improves the base planner from **60.67/65.25** to **83.51/78.38** on the challenging Test14-hard split under NR/R protocols, without changing the planner architecture or adding deployment-time rule refinement.

## Iterative Improvement

Detailed Test14-hard metrics across five ROCL updates:

| ROCL round | Score | Collisions | TTC | Drivable | Comfort | Progress |
|---:|---:|---:|---:|---:|---:|---:|
| 0 Base | 60.67 | 75.55 | 64.76 | 90.81 | 88.24 | 94.81 |
| 1 | 71.64 | 91.36 | 84.92 | 95.95 | 90.81 | 74.68 |
| 2 | 78.63 | 91.54 | 82.72 | 95.59 | 89.34 | 87.78 |
| 3 | 80.70 | 94.85 | 84.56 | 95.22 | 90.44 | 88.87 |
| 4 | 83.00 | 94.49 | 85.66 | 97.43 | 92.28 | 90.16 |
| 5 | 83.52 | 93.20 | 85.29 | 97.43 | 91.54 | 91.80 |

## Setup

R2LPL is trained and evaluated on nuPlan. Please refer to the official [nuplan-devkit](https://github.com/motional/nuplan-devkit) repository for dataset setup details. Make sure the `NUPLAN_DATA_ROOT` and `NUPLAN_MAPS_ROOT` environment variables are set. The expected data layout is `${NUPLAN_DATA_ROOT}/nuplan-v1.1/trainval` and `${NUPLAN_DATA_ROOT}/nuplan-v1.1/test` for nuPlan `.db` files, and `${NUPLAN_MAPS_ROOT}/` for map files.

Once nuPlan is importable in your environment, install R2LPL with:

```bash
git clone https://github.com/Engibacter/R2LPL.git

cd R2LPL

pip install -r requirements.txt

pip install -e .
```

## Reproduction Assets

Download the released base-planner checkpoint and planner anchors to the default paths if you wish to reproduce the experiments. Otherwise, cache the data and train the base planner manually.

From the repository root:

```bash
cd /path/to/R2LPL

mkdir -p results/checkpoints/muvo_base_model results/planner_anchors

wget -O results/checkpoints/muvo_base_model/last.ckpt \
  https://github.com/Engibacter/R2LPL/releases/download/v0.1.0-assets/last.ckpt

wget -O results/planner_anchors/planner_anchors_M4096s_T4.0_step20_full.npy \
  https://github.com/Engibacter/R2LPL/releases/download/v0.1.0-assets/planner_anchors_M4096s_T4.0_step20_full.npy
```

The main rollout continual-learning script uses these locations by default:

```bash
python run/script/run_rollout_cl_auto.py --dry-run --rounds 1
```

## Quick Reproduction

Detailed resource planning, additional evaluation commands, nuBoard usage, ablation instructions, and custom training notes are available in the [reproduction guide](docs/reproduction.md).

To reproduce the main experimental results, choose an appropriate worker configuration and specify the scenario filter and scenario builder for the target benchmark:

```bash
cd /path/to/R2LPL

python run/script/run_rollout_cl_auto.py \
  --rounds 5 \
  --scenario-filter test14-hard \
  --scenario-builder nuplan_test \
  --rollout-num-workers 32 \
  --retrieval-num-workers 48 \
  --sim-worker-threads-per-node 32 \
  --gpus-per-worker 0.0625 \
  --sim-gpus-per-worker 0.0625
```

If you want to use a scenario filter such as `val14` that is split from trainval data, use `--scenario-builder nuplan_trainval` instead. To resume from a previous round, use `--resume-from X` and R2LPL will resume from the checkpoint and cache generated by round `X`.

See the reproduction guide for [resource planning](docs/reproduction.md#resource-planning), [additional evaluation](docs/reproduction.md#additional-evaluation), [nuBoard usage](docs/reproduction.md#nuboard), [ablation experiments](docs/reproduction.md#ablation-experimental-results), and [custom model training](docs/reproduction.md#custom-model-training).

## Reproducibility Notes

The released checkpoints have been verified to reproduce similar closed-loop performance trends to those reported in the paper. We are actively improving the documentation and will continue to update the repository with cleaner scripts and additional instructions.

## Citation

If you find this project useful, please consider citing our paper:

```bibtex
@misc{gong2026r2lpl,
  title={Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving},
  author={Gong, Cheng and Wang, Haoyang and Lu, Chao and Li, Zirui and Gong, Jianwei},
  year={2026},
  eprint={2606.30537},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2606.30537}
}
```

## License

This project is released under the Apache License 2.0.

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