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..
How to Explore with Belief: State Entropy Maximization in POMDPs
Authors: Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide an empirical corroboration of the proposed methods and reported claims (results reported in Figure 2 and 3). |
| Researcher Affiliation | Academia | 1Politecnico di Milano, Milan, Italy. 2Technion Israel Institute of Technology, Haifa, Israel. |
| Pseudocode | Yes | Algorithm 1 Reg-PG for Max Ent POMDPs |
| Open Source Code | Yes | The code is available at this link. |
| Open Datasets | No | The paper uses custom Gridworld environments ("5x5-Gridworld", "6x6-Gridworld") without providing a specific link, DOI, formal citation, or stating their public availability as a dataset. |
| Dataset Splits | No | The paper describes the experimental environments and reports results over multiple runs but does not explicitly provide training, validation, or test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | Table 1. Wall-clock time [sec] of the main experiments on general-purpose CPUs. No specific CPU or GPU models, memory details, or other hardware specifications are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks, solvers) are mentioned in the paper. |
| Experiment Setup | Yes | The learning rate was selected as α = 0.3. The batch size was selected to be N = 10 after tuning. As for the time horizon, T = S in all the experiments. This makes the exploration task more challenging as every state can be visited at most once. The best regularization term ρ was found to be approximately equal to 0.02. |