Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
Authors: Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments show that the proposed path-integral control based variational inference method leads to tighter lower bounds in statistical model learning of sequential data. |
| Researcher Affiliation | Academia | Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, and Han-Lim Choi Department of Aerospace Engineering & KI for Robotics, KAIST Daejeon 305-701, Republic of Korea {{jsha, yjpark, hjchae, sspark}@lics., hanlimc@}kaist.ac.kr |
| Pseudocode | Yes | We implemented APIAE with Tensorflow [Abadi et al., 2016]; the pseudo code and algorithmic details of APIAE are given in the Appendix C. |
| Open Source Code | Yes | The supplementary video1 and the implementation code2 are available online. 2https://github.com/yjparkLiCS/18-NeurIPS-APIAE |
| Open Datasets | Yes | We utilized human motion capture data from the Carnegie Mellon University motion capture (CMU mocap) database for the learning; |
| Dataset Splits | No | The paper specifies training and test data amounts but does not mention a separate validation split for either experiment (e.g., Pendulum: '3000 and 500 data are used for training and test, respectively.' Mocap: '1043 and 173 data are used for training and test, respectively.'). |
| Hardware Specification | No | The paper mentions that 'the whole operations were parallelized with GPU' but does not specify any particular GPU model, CPU, or other hardware components. |
| Software Dependencies | No | The paper mentions 'We implemented APIAE with Tensorflow [Abadi et al., 2016]' but does not provide a specific version number for TensorFlow or any other software. |
| Experiment Setup | Yes | We set our APIAE parameters as L=8, R=4, and K=10 during experiments. |