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..
Controlling Underestimation Bias in Reinforcement Learning via Quasi-median Operation
Authors: Wei Wei, Yujia Zhang, Jiye Liang, Lin Li, Yyuze Li8621-8628
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on the discrete and continuous action tasks, and results show that our method outperforms the state-of-the-art methods. In this section, we empirically evaluate our method in the discrete and continuous action environments. |
| Researcher Affiliation | Academia | Wei Wei, Yujia Zhang, Jiye Liang*, Lin Li, Yuze Li School of Computer and Information Technology, Shanxi University, Taiyuan 030006, P.R. China EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: QMQ algorithm; Algorithm 2: QMD3 algorithm |
| Open Source Code | No | The paper does not provide any explicit statement about releasing code or a link to a code repository. |
| Open Datasets | Yes | For the discrete action environments, we choose 6 games from Gym (Brockman et al. 2016), PLE (Tasο¬ 2016), and Min Atar (Young and Tian 2019): Lunarlander-v2, Catcherv0, Pixelcopter-v0, Asterix-v0, Breakout-v0, and Space Invaders-v0 to evaluate QMQ. ... For the continuous action environments, we compare the proposed QMD3... on 8 Mu Jo Co tasks (Todorov, Erez, and Tassa 2012): Inverted Pendulum-v2 (IP), Inverted Double Pendulum-v2 (IDP), Reacher-v2, Hopperv3, Half Cheetah-v3, Walker2d-v3, Ant-v3, and Humanoidv3 |
| Dataset Splits | No | The paper describes training processes within environments and evaluations of performance, but does not specify 'training/test/validation dataset splits' with percentages or sample counts as typically understood in supervised learning. It mentions 'validation' in contexts like convergence proof or improving exploration, but not for dataset partitioning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions environments like OpenAI Gym, PLE, MinAtar, and MuJoCo, but does not provide specific version numbers for software dependencies or libraries used for implementation. |
| Experiment Setup | No | The paper states that 'More detailed information about the rendering of the environment, hyper-parameters, and implementation details can be found in Appendix D.A and Appendix E.' and 'For more details about the rendering of the environment, hyper-parameters, and implementation details, please refer to Appendix D.B and Appendix E.', indicating that these specific details are not present in the main text. |