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..
Truly No-Regret Learning in Constrained MDPs
Authors: Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we provide numerical evaluations of our algorithm in simple environments. We perform numerical simulations of our algorithms and compare them to their unregularized counterparts (Efroni et al., 2020). |
| Researcher Affiliation | Academia | 1EPFL 2ETH Zurich 3University of Zurich |
| Pseudocode | Yes | Algorithm 1 Regularized Primal-Dual Algorithm with Optimistic Exploration |
| Open Source Code | Yes | We provide the code in the supplementary material. |
| Open Datasets | No | We consider a randomly generated CMDP with deterministic rewards and unknown transitions. |
| Dataset Splits | No | No specific dataset splits (training, validation, test) were mentioned as the environment is randomly generated for simulation and interaction. |
| Hardware Specification | Yes | All simulations were performed on a Mac Book Pro 2.8 GHz Quad-Core Intel Core i7. |
| Software Dependencies | No | No specific software names with version numbers were mentioned. |
| Experiment Setup | Yes | For the vanilla algorithms, we run for K = 4000 episodes for each step size η {0.05, 0.075, 0.1, 0.125, 0.15, 0.2}, which we observed to be a reasonable range across CMDPs when fixing the number of episodes. Similarly, for the regularized algorithms, we perform the same parameter search across all pairs of step size η {0.05, 0.1, 0.2} and regularization parameter τ {0.01, 0.02}, totaling a number of six hyperparameter configurations as well. We always set λmax = 6, which did not play a role in our simulations as long as it was chosen sufficiently large. We use exploration bonuses 0.08 nh(s, a) 1/2. |