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..
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning
Authors: Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav Sukhatme, Stefan Schaal, Sergey Levine
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our simulation and real-world experiments demonstrate that this method can solve challenging manipulation tasks with comparable or better performance than model-free methods while maintaining the sample efficiency of model-based methods. |
| Researcher Affiliation | Academia | 1University of Southern California, Los Angeles, CA, USA 2Max Planck Institute for Intelligent Systems, T ubingen, Germany 3University of California Berkeley, Berkeley, CA, USA. |
| Pseudocode | Yes | Algorithm 1 PILQR algorithm |
| Open Source Code | Yes | The performance of each method can be seen in our supplementary video.2 https://sites.google.com/site/icml17pilqr |
| Open Datasets | Yes | The reacher task from Open AI gym (Brockman et al., 2016) |
| Dataset Splits | No | The paper mentions 'test conditions' for evaluation but does not specify the training, validation, or testing split percentages or counts for the datasets used. |
| Hardware Specification | Yes | To evaluate our method on a real robotic platform, we use a PR2 robot (see Figure 1) to learn the following tasks: |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | Additional experimental setup details, including the exact cost functions, are provided in Appendix 8.3.1. (...) Our TVLG policies consist of 100 time steps and we control our robot at a frequency of 20 Hz. (...) In all of the real robot experiments, policies are updated every 10 rollouts and the final policy is obtained after 20-25 iterations, which corresponds to mastering the skill with less than one hour of experience. |