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..
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
Authors: Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results are provided to complement the theoretical analysis. Numerical experiments have been carried out to evaluate OTI. All the results are averaged over 100 runs of horizon T = 105 and the agents perform the α-UCB algorithm specified in Section 5.1 with α = 2. |
| Researcher Affiliation | Academia | Chengshuai Shi University of Virginia EMAIL Haifeng Xu University of Virginia EMAIL Wei Xiong The Hong Kong University of Science and Technology EMAIL Cong Shen University of Virginia EMAIL |
| Pseudocode | Yes | Algorithm 1 OTI: Principal |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the methodology is released. |
| Open Datasets | No | The paper describes experiments run on simulated environments (e.g., 'toy example of M = 2 agents and K = 3 arms', 'random local instances with 30 arms are generated') rather than a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper discusses simulation parameters but does not specify explicit training, validation, or test dataset splits in the conventional sense for a supervised learning problem. The environment is simulated for multi-armed bandit scenarios. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions agents running the 'α-UCB algorithm', but does not specify any software names with version numbers used for implementation (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | All the results are averaged over 100 runs of horizon T = 105 and the agents perform the α-UCB algorithm specified in Section 5.1 with α = 2. First, with a toy example of M = 2 agents and K = 3 arms, the ineffectiveness of not incentivizing is illustrated. Under different M, random local instances with 30 arms are generated to compose global instances with min [4.5, 5.5] 10 3. |