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..
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
Authors: Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform a thorough experimental evaluation of GAE analysing its statistical and computational efficiency on two tasks where the unknown quantity f represents: (1) the amount of pollutant emerging from a point source (see Fig. 1), and (2) the toxicity of chemical compounds generated from a set of base elements (see Fig. 2f). |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Zurich, Zurich, Switzerland 2ETH AI Center, Zurich, Switzerland 3Technion, Haifa, Israel. |
| Pseudocode | Yes | Algorithm 1 Geometric Active Exploration (GAE) |
| Open Source Code | No | The paper does not contain an explicit statement about the release of its source code or a link to a code repository. |
| Open Datasets | No | The paper describes the construction of simulated environments and data within the paper (e.g., 'S = 240 states' for pollutant diffusion and 'S = 363 states' for chemical compounds) but does not provide access information (link, DOI, specific citation) for a publicly available dataset. |
| Dataset Splits | No | The paper describes simulated environments and data generation processes, but does not specify training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper mentions computational time was measured but does not specify the exact hardware (CPU, GPU models, or specific machine configurations) used for the experiments. |
| Software Dependencies | No | The paper mentions 'Python' and a 'standard time library in Python' but does not provide specific version numbers for these or any other key software dependencies (e.g., machine learning frameworks, solvers, or additional libraries). |
| Experiment Setup | Yes | The smoothness parameter was chosen to be η = 0.001, and δ = 0.01 for both, deterministic and stochastic dynamics. Furthermore, we found that in practice, a constant number of interactions τk = τ for all the K iterations of GAE works well, especially for remarkably low τ. In this setting, we chose τ = 3... To update the abstract state-action frequency λk+1, we also use a constant update step of 0.005/S. The initial state of the agent was chosen on the outermost circle. n as 210, resulting in K = 70 iterations of GAE. All the experiments were repeated over 15 random seeds. |