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..
Exploiting Multiple Abstractions in Episodic RL via Reward Shaping
Authors: Roberto Cipollone, Giuseppe De Giacomo, Marco Favorito, Luca Iocchi, Fabio Patrizi
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we prove that the method guarantees optimal convergence and we demonstrate its effectiveness experimentally. and (iv) an experimental analysis showing that our approach significantly improves sample-efficiency and that modelling errors yield only a limited performance degradation. |
| Researcher Affiliation | Collaboration | Roberto Cipollone1, Giuseppe De Giacomo1,2, Marco Favorito3, Luca Iocchi1, Fabio Patrizi1 1DIAG, Universit a degli Studi di Roma La Sapienza , Italy 2Department of Computer Science, University of Oxford, U.K. 3Banca d Italia, Italy |
| Pseudocode | Yes | Algorithm 1: Main algorithm |
| Open Source Code | Yes | Code available at https://github.com/cipollone/multinav2 |
| Open Datasets | No | The paper describes custom-built environments like the '4-rooms' and '8-rooms' domain. While the code for the environment might be implied to be in the linked GitHub repo, the paper does not provide concrete access information (link, DOI, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset as a standalone dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of algorithms like Q-learning, Delayed Q-learning, and Dueling DQN, but does not provide specific ancillary software details, such as library or solver names with version numbers (e.g., Python version, PyTorch/TensorFlow versions). |
| Experiment Setup | No | The paper mentions 'Further training details can be found in the appendix' but does not include specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |