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..
Learning from Active Human Involvement through Proxy Value Propagation
Authors: Zhenghao (Mark) Peng, Wenjie Mo, Chenda Duan, Quanyi Li, Bolei Zhou
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Human-in-the-loop experiments show the generality and efficiency of our method. |
| Researcher Affiliation | Academia | University of California, Los Angeles, University of Edinburgh |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Demo video and code are available at: https://metadriverse.github.io/pvp. |
| Open Datasets | Yes | We conduct experiments on various control tasks with different observation and action spaces. For continuous action space, we use three driving environments, Meta Drive safety benchmark [25], CARLA Town01 [8], and a customized driving environment built upon Grand Theft Auto V (GTA V), a popular video game. ... For discrete action space, we use Mini Grid Two Room task [4]. |
| Dataset Splits | Yes | In Meta Drive, there exists a split of training and test environments, and we present the performance of the learned agent in a held-out test environment. |
| Hardware Specification | Yes | All experiments with humans are conducted on a local computer with an Nvidia Ge Force RTX 3080. |
| Software Dependencies | No | We implement most of the code with Stable-Baselines3 [37]. |
| Experiment Setup | Yes | Hyper-parameters and other details are given in Appendix E and G. ... Table 7: PVP (Meta Drive) Hyper-parameter Value Discounted Factor γ 0.99 τ for Target Network Update 0.005 Learning Rate 0.0001 Steps before Learning Start 100 Steps per Iteration 1 Gradient Steps per Iteration 1 Train Batch Size 100 Q Value Bound 1 |