Goal-Conditioned Reinforcement Learning with Imagined Subgoals
Authors: Elliot Chane-Sane, Cordelia Schmid, Ivan Laptev
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on complex robotic navigation and manipulation tasks and show that it outperforms existing methods by a large margin. |
| Researcher Affiliation | Academia | 1Inria, Ecole normale suprieure, CNRS, PSL Research University, 75005 Paris, France. |
| Pseudocode | Yes | Algorithm 1 RL with imagined subgoals |
| Open Source Code | Yes | Code is available on the project webpage https://www. di.ens.fr/willow/research/ris/. |
| Open Datasets | No | The paper describes simulated environments ('Ant navigation', 'Vision-based robotic manipulation') and experimental setups but does not provide concrete access information (link, DOI, specific repository, or formal citation with author/year for a standard public dataset) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes how initial states and goals are sampled during training and evaluation, but does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed to reproduce data partitioning for train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, or detailed computer specifications) used for running its experiments in the main text. |
| Software Dependencies | No | The paper mentions using neural networks and various algorithms but does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment in the main text. |
| Experiment Setup | No | The paper states 'Additional implementation details are given in Appendix B' and 'We provide additional details about the hyperparameters used in our experiments in Appendix D.' This indicates the details are not in the main text. |