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..
Fast Model Identification via Physics Engines for Data-Efficient Policy Search
Authors: Shaojun Zhu, Andrew Kimmel, Kostas E. Bekris, Abdeslam Boularias
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations, performed both in simulation and on a real robotic manipulation task, indicate that the proposed strategy results in an overall timeefficient, integrated model identification and learning solution, which significantly improves the dataefficiency of existing policy search algorithms. |
| Researcher Affiliation | Academia | Shaojun Zhu, Andrew Kimmel, Kostas E. Bekris, Abdeslam Boularias Department of Computer Science, Rutgers University, New Jersey, USA |
| Pseudocode | Yes | Algorithm 1: Main Loop |
| Open Source Code | No | The paper mentions the use of third-party open-source tools (MuJoCo, OpenAI Gym, rllab) but does not state that the code for the specific methodology presented in this paper is open-source or provide a link to it. |
| Open Datasets | Yes | The simulation experiments are performed in Open AI Gym [Brockman et al., 2016] with the Mu Jo Co simulator1. |
| Dataset Splits | No | The paper does not provide specific percentages, sample counts, or explicit descriptions of how the data was split into training, validation, and test sets. |
| Hardware Specification | No | The paper mentions using specific robots (Motoman, Baxter) and a simulator (MuJoCo) but does not provide any details about the computational hardware (e.g., CPU, GPU models, memory) used for running the simulations or training. |
| Software Dependencies | No | The paper names software components like Open AI Gym, MuJoCo, and rllab, but it does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | The policy network has 2 hidden layers with 32 neurons each. |