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..
Simplifying Latent Dynamics with Softly State-Invariant World Models
Authors: Tankred Saanum, Peter Dayan, Eric Schulz
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the PLSM s effect on planning algorithms ability to learn policies in continuous control tasks. |
| Researcher Affiliation | Academia | Tankred Saanum1 Peter Dayan1,2 Eric Schulz1,3 1Max Planck Institute for Biological Cybernetics, 2University of Tübingen 3 Helmholtz Institute for Human-Centered AI, Helmholtz Center Munich, Neuherberg, Germany EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | We will release code with model implementation upon publication. |
| Open Datasets | Yes | We evaluated the efficacy of parsimonious dynamics for control in five state-based continuous control tasks from the Deep Mind Control Suite (DMC) [11]. ... We trained the PLSM augmented SPR algorithm on 100k environment steps across 5 seeds on all 26 Atari games. ... Additionally we created an environment based on the d Sprite dataset [18]... Lastly, we evaluate PLSM on a dynamic object interaction dataset with realistic textures and physics without actions, MOVi-E [19] |
| Dataset Splits | No | No explicit statement detailing specific training/validation/test splits (e.g., percentages, sample counts, or explicit predefined splits) was found. |
| Hardware Specification | Yes | We ran all experiments reported in the paper on compute nodes with 2 Nvidia A100 GPUs. |
| Software Dependencies | No | The paper mentions software components like PyTorch (implicitly via `torch` import), ReLU activation, and Adam optimizer, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Table 2: Contrastive model hyperparameters. Hidden units 512 Batch size 512 MLP hidden layers 2 Latent dimensions |zt| 50 Query dimensions |ht| 50 Regularization coefficient β 0.1 Margin λ 1 Learning rate 0.001 Activation function Re LU [50] Optimizer Adam [51] |