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..
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Authors: Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we report numerical simulations supporting our theoretical findings and showing how SCAL significantly outperforms UCRL in MDPs with large diameter and small span. |
| Researcher Affiliation | Collaboration | 1Seque L Team, INRIA Lille, France 2Facebook AI Research, Paris, France 3Montanuniversit at Leoben, Austria. |
| Pseudocode | Yes | Figure 1. The general structure of optimistic algorithms for RL. and Figure 3. Algorithm SCOPT. |
| Open Source Code | Yes | The code is available on Git Hub. |
| Open Datasets | No | The paper uses a 'simple but descriptive three-state domain' and specifies reward distributions (Bernoulli) but does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open 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 that 'The code is available on Git Hub.' but does not list specific ancillary software components with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | In all the experiments, we noticed that perturbing the extended MDP was not necessary to ensure convergence of SCOPT and so we set ηk = 0. We also set γk = 0 to speed-up the execution of SCOPT (see stopping condition in Fig. 3). |