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..
Optimizing for the Future in Non-Stationary MDPs
Authors: Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip Thomas
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents empirical evaluations using several environments inspired by real-world applications that exhibit non-stationarity. |
| Researcher Affiliation | Collaboration | 1University of Massachusetts, MA, USA. 2Adobe Research, CA, USA. 3University of Alberta, AB, Canada. |
| Pseudocode | Yes | We provide a sketch of our proposed Prognosticator procedure for optimizing the future performance of the policy in Algorithm 1. |
| Open Source Code | Yes | Code for our algorithm can be obtained using the following link: https://github.com/yashchandak/OptFuture_NSMDP. |
| Open Datasets | Yes | This environment is based on an open-source implementation (Xie, 2019) of the FDA approved Type-1 Diabetes Mellitus simulator (T1DMS) (Man et al., 2014) for treatment of Type-1 Diabetes. |
| Dataset Splits | No | The paper mentions running multiple trials and hyper-parameter sweeps, but it does not explicitly state specific train/validation/test dataset splits or their sizes. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers required to replicate the experiments. |
| Experiment Setup | Yes | Input Learning-rate η, time-duration δ, entropy-regularizer λ (from Algorithm 1). In our experiments, we noticed that the proposed algorithm is particularly sensitive to the value of the entropy regularizer λ. |