One After Another: Learning Incremental Skills for a Changing World
Authors: Nur Muhammad Mahi Shafiullah, Lerrel Pinto
ICLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate experimentally that in both evolving and static environments, incremental skills significantly outperform current state-of-the-art skill discovery methods on both skill quality and the ability to solve downstream tasks. |
| Researcher Affiliation | Academia | Nur Muhammad (Mahi) Shafiullah EMAIL New York University Lerrel Pinto EMAIL New York University |
| Pseudocode | Yes | H DISK PSEUDO CODE Algorithm 2 Pseudocode for DISk training routine for the Mth skill. |
| Open Source Code | Yes | Videos for learned skills and code are made public on: https://notmahi.github.io/disk. |
| Open Datasets | Yes | for stationary tasks we use standard Mu Jo Co environments from Open AI Gym (Todorov et al., 2012; Brockman et al., 2016): Half Cheetah, Hopper, Ant, and Swimmer |
| Dataset Splits | No | No explicit percentages or counts for training/validation/test dataset splits are provided. The paper describes data collection into replay buffers for training, but not a fixed split for dataset partitioning like in supervised learning. |
| Hardware Specification | Yes | All of our experiments were run between a local machine with an AMD Threadripper 3990X CPU and two NVIDIA RTX 3080 GPUs running Ubuntu 20.04, and a cluster with Intel Xeon CPUs and NVIDIA RTX 8000 GPUs, on a Ubuntu 18.04 virtual image. |
| Software Dependencies | Yes | We base our implementation on the Py Torch implementation of SAC by Yarats & Kostrikov (2020). |
| Experiment Setup | Yes | D IMPLEMENTATION DETAILS AND HYPERPARAMETERS |