TorchRL: A data-driven decision-making library for PyTorch
Authors: Albert Bou, Matteo Bettini, Sebastian Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fabritiis, Vincent Moens
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we experimentally demonstrate its reliability and flexibility and show comparative benchmarks to demonstrate its computational efficiency. |
| Researcher Affiliation | Collaboration | Albert Bou UPF, Acellera Matteo Bettini University of Cambridge Sebastian Dittert UPF Vikash Kumar Meta AI Shagun Sodhani Meta AI Xiaomeng Yang Meta AI Gianni De Fabritiis ICREA, UPF, Acellera Vincent Moens Py Torch, Meta vmoens[at]meta.com |
| Pseudocode | No | Figures 5 and 6 contain code examples but are not presented as pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Torch RL fosters long-term support and is publicly available on Git Hub for greater reproducibility and collaboration within the research community. The code is open-sourced on Git Hub. |
| Open Datasets | Yes | Torch RL offers a range of downloadable datasets for this purpose (such as D4RL (Fu et al., 2020) or Open ML (Vanschoren et al., 2013)). |
| Dataset Splits | No | The paper mentions using D4RL datasets and MuJoCo/Atari environments but does not specify explicit train/validation/test splits (e.g., percentages or counts) within the paper itself. |
| Hardware Specification | Yes | These experiments were run on an AWS cluster with a single node (96 CPU cores and 1 A100 GPUs per run). |
| Software Dependencies | Yes | Compatibility with all versions of Gym is guaranteed starting from v0.13, including the latest transition to Gymnasium. We measured Tensor Dict overhead for the lastest release at the time of writing the manuscript (v0.2.1) against other existing solutions. |
| Experiment Setup | Yes | Tables 6, 7 and 8 display all hyperparameters values and network architecture details required to reproduce our online RL results. Table 9 displays all hyperparameter values and network architecture details required to reproduce our offline RL results. |