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..
VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning
Authors: Che Wang, Xufang Luo, Keith Ross, Dongsheng Li
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a set of challenging hand manipulation tasks with sparse reward and realistic visual inputs, compared to the previous SOTA, VRL3 achieves an average of 780% better sample efficiency. And on the hardest task, VRL3 is 1220% more sample efficient (2440% when using a wider encoder) and solves the task with only 10% of the computation. These significant results clearly demonstrate the great potential of data-driven deep reinforcement learning. |
| Researcher Affiliation | Collaboration | Che Wang1,2 Xufang Luo3 Keith Ross1 Dongsheng Li3 1 New York University Shanghai 2 New York University 3 Microsoft Research Asia, Shanghai, China |
| Pseudocode | Yes | We focus on the high-level ideas and provide additional technical details in Appendix A and pseudocode in appendix C. |
| Open Source Code | No | (Source code is being reviewed and cleaned and will be put on Github soon). We provide source code 2 and a full set of technical details to maximize reproducibility. (Footnote 2 points to https://sites.google.com/nyu.edu/vrl3) |
| Open Datasets | Yes | In stage 1, we learn from large, existing non-RL datasets such as the Image Net dataset. Note that for Adroit, we have a standard 25 expert demonstrations per task (collected by human users with VR) [66]. For DMC, we collect 25K data with fully trained Dr Qv2 agents to enable stage 2 training. |
| Dataset Splits | No | The paper does not explicitly provide specific percentages or counts for training/validation/test dataset splits, nor does it explicitly reference standard predefined splits for the RL environments used beyond general mention of ImageNet. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions building upon Dr Qv2 and standard libraries (e.g., for ResNet), but does not provide specific version numbers for software dependencies like Python, PyTorch, or other key packages. |
| Experiment Setup | Yes | We focus on the high-level ideas and provide additional technical details in Appendix A and pseudocode in appendix C. Let α be the learning rate for the policy network and the Q networks. Let βenc be the encoder learning rate scale, so that the encoder learning rate is αenc = βencα. For all tasks, we set a maximum Q target value. We also use Polyak averaging hyperparameter τ to update target networks, as is typically done. |