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..
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Authors: Dane Corneil, Wulfram Gerstner, Johanni Brea
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the Va ST agent on a series of navigation tasks implemented in the Viz Doom environment... We compared the performance of Va ST against two recently published sample efficient model free approaches: Neural Episodic Control (NEC) (Pritzel et al., 2017) and Prioritized Double DQN (Schaul et al., 2015). |
| Researcher Affiliation | Academia | Dane Corneil 1 Wulfram Gerstner 1 Johanni Brea 1 Laboratory of Computational Neuroscience (LCN), School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, Ecole Polytechnique F ed erale de Lausanne, Switzerland. |
| Pseudocode | Yes | The pseudocode of Va ST, and of our implementation of prioritized sweeping, are in the Supplementary Material. |
| Open Source Code | Yes | The full code for Va ST can be found at https://github. com/danecor/Va ST/. |
| Open Datasets | Yes | We evaluated the Va ST agent on a series of navigation tasks implemented in the Viz Doom environment (see Figure 3A, Kempka et al. (2016)). |
| Dataset Splits | No | The paper describes evaluation over "test epochs" but does not provide specific training/validation/test dataset splits with percentages or sample counts, which is typical for static datasets rather than online reinforcement learning environments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions software environments like "Viz Doom environment" and "Arcade Learning Environment" but does not provide specific version numbers for these or any other ancillary software components or libraries. |
| Experiment Setup | Yes | We use a multilayer perceptron (3 layers for each possible action) for the transition model pθT... and ...temperatures taken from those suggested in (Maddison et al., 2016): λ1 = 2/3 for the posterior distribution and λ2 = 0.5 for evaluating the transition log probabilities. We used two replay memory sizes (N = 100 000 and N = 500 000). |