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..
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Authors: Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our image-conditioned 3D object assembly of B3 in three scenarios: (i) MNIST construction, (ii) randomly-assembled object construction, and (iii) Model Net construction. For an evaluation metric, we measure the episode return or Io U between the constructed object and the desired target at the end of each episode: |
| Researcher Affiliation | Collaboration | 1POSTECH 2University of Guelph 3Vector Institute 4POSCO |
| Pseudocode | No | The paper describes the model and algorithms using mathematical formulations and textual descriptions but does not include a distinct pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | For an evaluation metric, we measure the episode return or Io U between the constructed object and the desired target at the end of each episode: (...) MNIST dataset (...) Model Net dataset [40] |
| Dataset Splits | Yes | In particular, 500 images from one of available classes are chosen, further divided into 400 samples for a training dataset and 100 samples for a test dataset. (...) we sample images from 800 target objects for a training dataset while 200 target objects are used for a test dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using “Open AI Gym” and the “PPO algorithm [35]” but does not provide specific version numbers for these or other software libraries/frameworks. |
| Experiment Setup | No | The paper mentions using the PPO algorithm and optimizing a clipped surrogate objective, but it does not provide concrete values for hyperparameters such as learning rate, batch size, or number of epochs in the main text. |