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..
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
Authors: Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additional experimental results further corroborate the effectiveness of Het PEVI. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of Virginia 2Department of Mathematics, The Hong Kong University of Science and Technology 3Department of Electrical Engineering, The Pennsylvania State University. |
| Pseudocode | Yes | Algorithm 1 Het PEVI |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The data source MDPs are randomly generated through a set of independent Dirichlet distributions (Marchal & Arbel, 2017). |
| Dataset Splits | No | The paper describes its simulation setup and data generation process but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes the simulation environment and parameters (e.g., S=2, A=20, H=20 for the target MDP) but does not specify any hardware details such as CPU, GPU, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions generating data using 'Dirichlet distributions (Marchal & Arbel, 2017)' but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | The target MDP is set to have H = 20 steps, S = 2 states (labelled as {1, 2}), A = 20 actions (labelled as {1, 2, , 20}). The reward and transitions are specified as follows: h [H], rh(s, a) = ( 0.9 if (s, a) = (1, 1) 0.1 otherwise h [H], Ph(1|s, a) = ( 0.9 if (s, a) = (1, 1) 0.5 otherwise. h [H], Ph(1|s, a) = ( 0.1 if (s, a) = (1, 1) 0.5 otherwise. The rewards of the data source are independently sampled from Bernoulli distributions, i.e., rh,l(s, a) Bernoulli(rh(s, a)), while the transitions are independently generated with standard Dirichlet distributions (Marchal & Arbel, 2017). The behavior policy is shared by all data sources, which at each (s, h) S [H], selects action 1 with probability 0.2 and otherwise randomly chooses from other actions. |