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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Learning Latent Dynamic Robust Representations for World Models
Authors: Ruixiang Sun, Hongyu Zang, Xin Li, Riashat Islam
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation demonstrates significant performance improvements over existing methods in a range of visually complex control tasks such as Maniskill (Gu et al., 2023) with exogenous distractors from the Matterport environment. |
| Researcher Affiliation | Collaboration | 1Beijing Institute of Technology, China 2Dream Fold AI, Canada. |
| Pseudocode | Yes | Algorithm 1 HRSSM |
| Open Source Code | Yes | Our code is avaliable at https://github.com/ bit1029public/HRSSM. |
| Open Datasets | Yes | We perform our experiments in three distinct settings: i) a set of Mu Jo Co tasks (Todorov et al., 2012) provided by Deepmind Control(DMC) suite (Tassa et al., 2018), ii) a variant of Deep Mind Control Suite where the background is replaced with grayscale natural videos from Kinetics dataset (Kay et al., 2017), termed as Distracted Deep Mind Control Suite (Zhang et al., 2018), and iii) a benchmark based on the Maniskill2 (Gu et al., 2023), enhanced with realistic images of human homes (Chang et al., 2017) as backgrounds and was introduced in (Zhu et al., 2023). |
| Dataset Splits | No | The paper describes experiments conducted in various environments but does not provide explicit training, validation, or test dataset splits in terms of percentages or sample counts. Data is typically generated through interaction with the environment in RL. |
| Hardware Specification | Yes | We compare the wall-clock traning time of our method and Dreamer V3 in the Realistic Maniskill environment, with the use of a sever with NVidia A100SXM4 (40 GB memory) GPU. |
| Software Dependencies | No | The paper mentions using an 'unofficial open-sourced pytorch version of Dreamer V3(NM512, 2023)', but it does not specify the version numbers for PyTorch or other key software components used for reproducibility. |
| Experiment Setup | Yes | Table 3. Our model’s hyperparameters, which are the same across all tasks in DMControl and Realistic Maniskill. This table lists various hyperparameters such as Replay capacity (FIFO) 10^6, Batch size B 16, Batch length T 64, Learning rate 10^-4, Mask ratio 50%, Cube spatial size h w 10 10, etc. |