These are Flow Map Q-Guidance (FMQ) agents trained with offline-to-online RL. Each checkpoint contains a flow map policy fine-tuned online for 1M steps using critic-guided trust-region optimization.

Checkpoints

12 environments x 5 random seeds = 60 checkpoints total.

Folder	Environment	Benchmark
`checkpoints/ctrp4/`	cube-triple-play-singletask-task4-v0	OGBench
`checkpoints/ctrp3/`	cube-triple-play-singletask-task3-v0	OGBench
`checkpoints/cdp4/`	cube-double-play-singletask-task4-v0	OGBench
`checkpoints/cdp3/`	cube-double-play-singletask-task3-v0	OGBench
`checkpoints/sc4/`	scene-play-singletask-task4-v0	OGBench
`checkpoints/sc5/`	scene-play-singletask-task5-v0	OGBench
`checkpoints/ag4/`	antmaze-giant-navigate-singletask-task4-v0	OGBench
`checkpoints/ag5/`	antmaze-giant-navigate-singletask-task5-v0	OGBench
`checkpoints/hm3/`	humanoidmaze-medium-navigate-singletask-task3-v0	OGBench
`checkpoints/hm4/`	humanoidmaze-medium-navigate-singletask-task4-v0	OGBench
`checkpoints/can/`	can-mh-low_dim	RoboMimic
`checkpoints/square/`	square-mh-low_dim	RoboMimic

Usage

bash

pip install huggingface_hub
python -c "from huggingface_hub import snapshot_download; snapshot_download('christoszi/flow-map-policies', local_dir='.')"

Then evaluate:

bash

python main.py --config configs/config.yaml \
  --eval_only --fmq_online \
  --restore_path=checkpoints/ctrp4/params_online_sd000.pkl \
  --env_name=cube-triple-play-singletask-task4-v0 --seed=0

Citation

bibtex

@article{ziakas2026fmq,
  title={Aligning Flow Map Policies with Optimal Q-Guidance},
  author={Ziakas, Christos and Russo, Alessandra and Bose, Avishek Joey},
  journal={arXiv preprint arXiv:2605.12416},
  year={2026},
}