Learning Latent Dynamics for Planning from Pixels
Authors: Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Pla Net on six continuous control tasks from pixels. |
| Researcher Affiliation | Collaboration | 1Google Brain 2University of Toronto 3Deep Mind 4Google Research 5University of Michigan. |
| Pseudocode | Yes | Algorithm 1: Deep Planning Network (Pla Net) |
| Open Source Code | Yes | Please visit https://danijar.com/planet for access to the code and videos of the trained agent. |
| Open Datasets | Yes | For our evaluation, we consider six image-based continuous control tasks of the Deep Mind control suite (Tassa et al., 2018), shown in Figure 1. |
| Dataset Splits | No | The paper describes iterative data collection and training but does not provide specific percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | Yes | The training time of 10 to 20 hours (depending on the task) on a single Nvidia V100 GPU compares favorably to that of A3C and D4PG. |
| Software Dependencies | No | The paper states "Our implementation uses Tensor Flow Probability (Dillon et al., 2017)" but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | We refer to the appendix for hyper parameters (Appendix A) and additional experiments (Appendices C to E). Besides the action repeat, we use the same hyper parameters for all tasks. |