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..
Optimistic Planning by Regularized Dynamic Programming
Authors: Antoine Moulin, Gergely Neu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a new method for optimistic planning in infinite-horizon discounted Markov decision processes... We use our method to recover known guarantees in tabular MDPs and to provide a computationally efficient algorithm for learning near-optimal policies in discounted linear mixture MDPs from a single stream of experience, and show it achieves near-optimal statistical guarantees. |
| Researcher Affiliation | Academia | 1Universitat Pompeu Fabra, Barcelona, Spain. Correspondence to: Antoine Moulin <EMAIL>, Gergely Neu <EMAIL>. |
| Pseudocode | Yes | The overall procedure is presented as Algorithm 1. ... Algorithm 2 RAVI-UCB for tabular MDPs. ... Algorithm 3 RAVI-UCB for linear mixture MDPs. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is openly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets or their public availability. The term 'train' is not used in the context of empirical data training. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with dataset splits. The term 'validation' is used once in 'validation of existing assumptions' which is not related to data splitting. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with specific hyperparameter values or training configurations. The 'setup' discussed refers to the theoretical model setup. |