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..
Observe Before Play: Multi-Armed Bandit with Pre-Observations
Authors: Jinhang Zuo, Xiaoxi Zhang, Carlee Joe-Wong7023-7030
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data and wireless channel traces show that C-MP-OBP and D-MPOBP outperform random heuristics and offline optimal policies that do not allow pre-observations. Our final contribution is to numerically validate our OBP, C-MP-OBP, and D-MP-OBP policies on synthetic reward data and channel availability traces. |
| Researcher Affiliation | Academia | Jinhang Zuo, Xiaoxi Zhang, Carlee Joe-Wong Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Observe-Before-Play UCB (OBP-UCB); Algorithm 2 Centralized Multi-Player OBP (C-MP-OBP); Algorithm 3 Distributed Multi-Player OBP (D-MP-OBP) |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology, nor does it include a direct link to a code repository. The link provided in the citation (Zuo et al. 2019) is to the paper itself. |
| Open Datasets | Yes | Experiments on synthetic data and wireless channel traces show that C-MP-OBP and D-MPOBP outperform random heuristics and offline optimal policies that do not allow pre-observations. Wang 2018. https://github.com/ANRGUSC/ Multichannel DQN-channel Model. |
| Dataset Splits | No | The paper mentions using 'synthetic data' and 'channel availability traces' but does not specify how these datasets were split into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with their version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | No | The paper describes the problem parameters (e.g., K arms, cost τ) and experiment duration ('after 5000 rounds'), but it does not specify concrete hyperparameter values for the algorithms (e.g., learning rates, batch sizes, optimizer settings) or other detailed system-level training configurations. |