Spatially Structured Recurrent Modules
Authors: Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present a selection of experiments to quantitatively evaluate S2RMs and gauge their performance against strong baselines on two data domains, namely video prediction from crops on the well-known bouncing-balls domain and multi-agent world modelling from partial observations in the challenging Starcraft2 domain. We also include qualitative visualizations on a grid-world task in Appendix A. Additional tables, results and supporting plots can be found in Appendix F. |
| Researcher Affiliation | Academia | Nasim Rahaman1,2 Anirudh Goyal2 Muhammad Waleed Gondal1 Manuel Wuthrich1 Stefan Bauer1 Yash Sharma3 Yoshua Bengio2,4 Bernhard Sch olkopf1 1Max-Planck Institute for Intelligent Systems T ubingen, 2Mila, Qu ebec, 3Bethgelab, Eberhard Karls Universit at T ubingen, 4Universit e de Montreal. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The second problem is that of multi-agent world modeling from partial observations in spatial domains, such as the challenging Starcraft2 domain (Samvelyan et al., 2019; Vinyals et al., 2017). |
| Dataset Splits | Yes | We use another 1K video sequences of the same length and the same number of balls as a held-out validation set. |
| Hardware Specification | Yes | We train all models with batch-size 8 (Starcraft2) or 32 (Bouncing Balls) on a single V100-32GB GPU (each). |
| Software Dependencies | Yes | We use Pytorch s (Paszke et al., 2019) Reduce LROn Plateau learning rate scheduler to decay the learning rate by a factor of 2 if the validation loss does not improve by at least 0.01% over the span of 5 epochs. |
| Experiment Setup | Yes | All models were trained using Adam Kingma & Ba (2014) with an initial learning rate 0.0003. We use Pytorch s (Paszke et al., 2019) Reduce LROn Plateau learning rate scheduler to decay the learning rate by a factor of 2 if the validation loss does not improve by at least 0.01% over the span of 5 epochs. We initially train all models for 100 epochs, select the best of three successful runs, fine-tune it for another 100 epochs, and finally select the checkpoint with the lowest validation loss (i.e. we early stop). We train all models with batch-size 8 (Starcraft2) or 32 (Bouncing Balls) on a single V100-32GB GPU (each). |