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..
No-Regret Exploration in Goal-Oriented Reinforcement Learning
Authors: Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce UC-SSP, the first no-regret algorithm in this setting, and prove a regret bound scaling as e O(DS ADK) after K episodes for any unknown SSP with S states, A actions, positive costs and SSP-diameter D... Finally, we support our theoretical findings with experiments in App. J. |
| Researcher Affiliation | Collaboration | 1Facebook AI Research, Paris, France 2Seque L team, Inria Lille Nord Europe, France. |
| Pseudocode | Yes | Algorithm 1 UC-SSP algorithm and Algorithm 2 EVISSP |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes using custom 'gridworld environments' for experiments, but it does not provide concrete access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We report our experimental results in App. J. for two different environments described in Fig. 2 (a) and Fig. 2 (b). For the parameters of UC-SSP, we set the confidence δ = 0.05 and use cmin = 1 and cmax = 10 for the general SSP case, as well as cmin = cmax = 1 for the uniform-cost SSP case. We average the regret over 50 independent runs and plot the average regret with 95% confidence intervals. For the discount factor γ of UCRL2 and UCRL2B, we choose γ = 0.95. For UCBVI, we use H = 100 as the fixed horizon. |