Fast Model Identification via Physics Engines for Data-Efficient Policy Search
Authors: Shaojun Zhu, Andrew Kimmel, Kostas E. Bekris, Abdeslam Boularias
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |