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Prepare IJCAI reproducibility artifact
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‎.github/workflows/ci.yml‎

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name: smoke
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on:
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push:
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pull_request:
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jobs:
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validate:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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- name: Compile scripts
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run: python -m py_compile scripts/*.py
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- name: Validate suite counts
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run: python scripts/validate_config.py
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- name: Dry-run smoke suite
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run: python scripts/reproduce_officeworld.py --suite smoke --dry-run

‎.gitignore‎

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.venv/
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__pycache__/
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*.py[cod]
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results/
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results_*.txt
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heatmaps/
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episodes/
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wandb/
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vi_cache/
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*.pkl
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*.pickle
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*.npy
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*.npz

‎CITATION.cff‎

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cff-version: 1.2.0
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message: "If you use this artifact in academic work, please cite the associated paper and software."
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type: software
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title: "QR-MAX IJCAI Reproducibility Artifact"
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version: "0.1.0"
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date-released: "2026-06-29"
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license: "Apache-2.0"
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repository-code: "https://github.com/Alee08/qrmax"
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authors:
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- family-names: "Trapasso"
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given-names: "Alessandro"
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email: "Ale.trapasso8@gmail.com"
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orcid: "https://orcid.org/0000-0001-5431-6607"
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preferred-citation:
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type: article
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title: "Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes"
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authors:
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- family-names: "Trapasso"
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given-names: "Alessandro"
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url: "https://arxiv.org/abs/2512.14617"
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keywords:
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- reinforcement-learning
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- reward-machines
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- non-markovian-reward
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- qrmax
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- reproducibility

‎LICENSE‎

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‎README.md‎

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# qrmax
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# QR-MAX IJCAI Artifact
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This repository contains the reproducibility layer for:
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> Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward
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> Decision Processes
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Paper: https://arxiv.org/abs/2512.14617
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Reusable implementation code lives in the companion library:
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https://github.com/Alee08/multiagent-rl-rm
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`qrmax` keeps only the experiment-facing pieces: pinned dependency metadata,
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OfficeWorld experiment matrices, launchers, reproducibility notes, and
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post-processing scripts.
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## Dependency Freeze
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The artifact is pinned to the companion-library release tag:
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- package: `multiagent-rl-rm`
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- package version: `0.3.0`
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- tag: `v0.3.0-ijcai2026`
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- commit: `7e0af546d6085177e61afacfab9c9437f5e2d243`
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Before making this repository public, push the tag to
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`Alee08/multiagent-rl-rm` or publish the matching PyPI release and update
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`requirements.txt` to `multiagent-rl-rm==0.3.0`.
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## Install
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```bash
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python -m venv .venv
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source .venv/bin/activate
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pip install -r requirements.txt
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```
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## Sanity Checks
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Validate configured suite sizes:
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```bash
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python scripts/validate_config.py
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```
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Dry-run the smoke suite:
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```bash
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python scripts/reproduce_officeworld.py --suite smoke --dry-run
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```
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Run a short smoke experiment:
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```bash
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python scripts/reproduce_officeworld.py --suite smoke
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```
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## Experiment Suites
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Suites are defined in `configs/officeworld_discrete.json`.
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| Suite | Runs | Purpose |
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| --- | ---: | --- |
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| `smoke` | 1 | Fast local/CI sanity check. |
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| `paper_main` | 80 | Main OfficeWorld configuration. |
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| `officeworld_discrete` | 1680 | Full encoded OfficeWorld sweep. |
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Run a suite:
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```bash
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python scripts/reproduce_officeworld.py --suite paper_main
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```
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Filter a larger suite without editing JSON:
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```bash
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python scripts/reproduce_officeworld.py \
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--suite officeworld_discrete \
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--algorithms QRMAX QRMAXRM \
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--maps map1 map2 \
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--seeds 0 1 2
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```
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The configured algorithm identifiers are the ones exposed by the frozen
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OfficeWorld runner: `QL`, `QRM`, `RMAX`, `RMAXRM`, `QRMAX`, `QRMAXRM`, `UCBVI`,
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and `OPSRL`.
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## Outputs
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The upstream OfficeWorld runner writes raw logs under `results/` and summary
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text files named `results_<map>_<experiment>.txt`. These files are ignored by
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Git.
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Generate a compact aggregate CSV:
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```bash
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python scripts/summarize_officeworld.py \
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--results-dir results \
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--output paper_results/officeworld_summary.csv
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```
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See `REPRODUCIBILITY.md` for the full workflow.
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## Citation
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Use `CITATION.cff` for repository metadata. The final IJCAI citation can be
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added once the camera-ready bibliographic metadata is available.

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