Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hierarchies of Reward Machines
Authors: Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda, Alessandra Russo
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Experimental Results |
| Researcher Affiliation | Collaboration | 1Imperial College London, UK 2ILASP Limited, UK 3Universitat Pompeu Fabra, Spain. |
| Pseudocode | Yes | We refer the reader to Appendix B.2 for the pseudo-code and step-by-step examples. |
| Open Source Code | Yes | The code is available at https://github. com/ertsiger/hrm-learning. |
| Open Datasets | Yes | The CRAFTWORLD domain (cf. Figure 1a) is used as a running example. In this domain, the agent ( ) can move forward or rotate 90 , staying put if it moves towards a wall. Locations are labeled with propositions from P = { , , , , , , , , , }. ... WATERWORLD (Karpathy, 2015; Sidor, 2016; Toro Icarte et al., 2018) consists of a 2D box containing 12 balls of 6 different colors (2 per color) each moving at a constant speed in a fixed direction. |
| Dataset Splits | No | The paper describes using multiple runs and instances for evaluation and a curriculum learning approach for tasks, but it does not specify explicit train/validation/test dataset splits with percentages or counts for reproduction in a traditional supervised learning sense. It focuses on task-instance pairs and average returns across episodes and runs. |
| Hardware Specification | Yes | All timed experiments ran on 3.40GHz Intel Core i7-6700 processors, while non-timed experiments have also run on 2.90GHz Intel Core i7-10700, 4.20GHz Intel Core i7-7700K, and 3.20GHz Intel Core i7-8700 processors. |
| Software Dependencies | No | The paper mentions software components and algorithms like 'Deep Q-networks', 'Double DQNs', and 'RMSprop', but it does not provide specific version numbers for software libraries, frameworks, or programming languages (e.g., PyTorch 1.x, Python 3.x). |
| Experiment Setup | Yes | Table 2: List of hyperparameters and their values. |