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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hierarchical Reinforcement Learning with Timed Subgoals
Authors: Nico Gürtler, Dieter Büchler, Georg Martius
NeurIPS 2021 | Venue PDF | 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 EMAIL |
| 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. |