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..
Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams
Authors: Erdem Biyik, Anusha Lalitha, Rajarshi Saha, Andrea Goldsmith, Dorsa Sadigh9296-9303
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analytically show that our proposed strategy achieves logarithmic regret, and provide extensive experiments involving human-AI and human-robot collaboration to validate our theoretical findings. |
| Researcher Affiliation | Academia | 1 Department of Electrical Engineering, Stanford University 2 Department of Electrical and Computer Engineering, Princeton University 3 Department of Computer Science, Stanford University |
| Pseudocode | Yes | Algorithm 1: Partner-Aware UCB: Follower; Algorithm 2: Partner-Aware UCB: Leader |
| Open Source Code | Yes | Code at: https://sites.google.com/view/partner-aware-ucb |
| Open Datasets | No | The paper describes conducting simulations with fixed or random reward means and human-subject studies using an experimental setup (burger stacking, slot machines), but does not use a traditional publicly available or open dataset with access information. |
| Dataset Splits | No | The paper describes experimental runs and user studies, including warm-starting with simulated agents, but it does not specify traditional training/validation/test dataset splits needed for reproduction in a typical ML context. |
| Hardware Specification | No | The paper mentions using a 'Fetch robot (Wise et al. 2016)' for human-subject studies but does not provide specific hardware specifications (e.g., CPU, GPU, memory) of the computational resources used for simulations or training models, nor the robot's internal processing hardware. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or programming languages used in the experiments or simulations. |
| Experiment Setup | Yes | Unless otherwise noted |A1|=|A2|=2, p1 =1, p2 =0.5, c(L) =c(F) =0.025 in these simulations. ... For Partner Aware UCB, we set L = 1, W = 25. ... We set, when relevant, L = 1, W = 2 and c(L) = c(F) = 0.01. ... we set c(i) = 0.025, L = 1, and W = 25 for all agents. |