Hierarchical Reinforcement Learning with Timed Subgoals
Authors: Nico Gürtler, Dieter Büchler, Georg Martius
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on a range of standard benchmarks and three new challenging dynamic reinforcement learning environments show that our method is capable of sample-efficient learning where an existing state-of-the-art subgoal-based HRL method fails to learn stable solutions. |
| Researcher Affiliation | Academia | Max Planck Institute for Intelligent Systems Tübingen, Germany {nguertler, dbuechler, gmartius}@tue.mpg.de |
| Pseudocode | No | The paper describes the algorithm steps in text but does not include a formally labeled pseudocode block or algorithm figure. |
| Open Source Code | Yes | Videos and code, including our algorithm and the proposed dynamic environments, can be found at https://github.com/martius-lab/Hi TS. |
| Open Datasets | Yes | The environments comply with Open AI s gym interface and are available at https://github.com/martius-lab/Hi TS. |
| Dataset Splits | No | The paper discusses training and evaluates on benchmarks but does not explicitly provide specific percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions software components like Soft Actor-Critic (SAC) and OpenAI Gym but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper states that 'Details about training and hyperparameter optimization are given in Suppl. C.' but these specific details are not provided within the main text. |