Murjani/ttpd-weights

TTP-D solver weights

Trained policy checkpoints for the study Fly, Pack, Drive: the Travelling Thief Problem with Drone.

A capacitated truck and a single-package drone operate from a common depot on a collection route. The truck's velocity decreases affinely with its accumulated load, so an early pickup penalises every subsequent arc. The drone launches from a node at which the truck is present, retrieves one item at an outlying customer, and rejoins the truck at a later rendezvous node; whichever vehicle arrives first waits, while the rental clock continues to run. The objective is to maximise collected profit net of a rental cost proportional to the makespan.

These checkpoints are the learned construction policies: an encoder–decoder model that embeds the instance once and emits a composite action at each node the truck reaches, trained offline with Proximal Policy Optimisation under a POMO group baseline.

Code: https://github.com/corbit-lab/ttpd. Benchmark instances, result tables, and behaviour-cloning datasets are in the companion dataset repository Murjani/ttpd-benchmarks.

Layout

<variant>/<model>/<family>/n<N>/best.pt

Variant is a280 (fixed drone endurance) or ttd300 (endurance-conditioned). Model is gat (attention encoder), mlp (the encoder ablation), or lisa (behaviour cloning). Family denotes the distribution the policy was trained on, which determines its applicability:

Family	Training distribution	Intended use
benchmark-tuned/	The five benchmark instances of that size	The reported headline results
sampled/	Randomly sampled a280 subsets	Generalisation; warm start for the above
specialists/	Early small-N runs	The N ≤ 20 rows, using dedicated small models

The benchmark-tuned policies warm-start from sampled/n20 of the same model family, as training from scratch does not converge within the budget at the larger sizes. Within a run, best.pt is the deliverable, selected by held-out beam evaluation; last.pt supports resumption; and best_milp.pt is selected by MILP gap rather than evaluation return.

Behaviour-cloned policies are stored at <variant>/lisa/behaviour_cloning/n<N>.pt, with a280/lisa/behaviour_cloning_initial/ and a280/lisa/gat-n50/ retained for provenance.

Deliverable checkpoint per model and size

Model and size	Checkpoint
a280 GAT, N = 5, 10	a280/gat/specialists/n5.pt, n10.pt
a280 GAT, N = 15, 20	a280/gat/sampled/n<N>/best.pt
a280 GAT, N = 30–100	a280/gat/benchmark-tuned/n<N>/best.pt
a280 MLP, N = 20	a280/mlp/sampled/n20/best.pt
a280 MLP, remaining sizes	a280/mlp/benchmark-tuned/n<N>/best.pt
ttd300 GAT	ttd300/gat/benchmark-tuned/n<N>/best.pt
LISA	<variant>/lisa/behaviour_cloning/n<N>.pt

The benchmark-tuned family also carries the smaller sizes so that a directory-based checkpoint argument resolves uniformly.

Loading

Checkpoints are torch.save payloads keyed under "policy":

import torch
from huggingface_hub import hf_hub_download

path = hf_hub_download("Murjani/ttpd-weights", "a280/gat/benchmark-tuned/n30/best.pt")
payload = torch.load(path, map_location="cpu", weights_only=False)
policy.load_state_dict(payload["policy"])

The repository code resolves these paths automatically through ttpd.hub.ensure_local, which returns an existing local file unchanged and downloads otherwise, so a machine that already holds the tree performs no network access.

manifest.json records, for every file, the path from which it was published together with its SHA-256 digest.

Citation

Please cite the accompanying paper. Reported results, including the comparison against the exact solver and the metaheuristics, are given there.

@inproceedings{murjani2026ttpd,
      title={Drive, Pack, Fly: The Travelling Thief Problem with Drone}, 
      author={Murjani, Kabir and Sobhanan, Abhay},
      year={2026},
      eprint={2608.16435},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2608.16435}, 
}