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..
Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
Authors: Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, Shixiang Shane Gu
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through principled mathematical derivations and careful experimental studies, our work lays a novel foundation from which to evaluate, analyze, and develop representation learning techniques in goal-based RL. |
| Researcher Affiliation | Collaboration | Jongwook Choi 1 Archit Sharma 2 Honglak Lee 1 3 Sergey Levine 4 5 Shixiang Shane Gu 4 Work done while an intern at Google. 1University of Michigan 2Stanford University 3LG AI Research 4Google Research 5University of California, Berkeley. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate the performance of several variants of VGCRL on standard locomotion tasks (Brockman et al., 2016). |
| Dataset Splits | No | The paper mentions evaluating performance on standard locomotion tasks but does not specify concrete training, validation, or test data splits. |
| Hardware Specification | No | The paper mentions using Mujoco for simulations but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments or training. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | Evaluation of Latent Goal-Reaching Metric on Mu Jo Co control suites, after a total of 10M environment steps of training. |