Cooperative Multi-player Bandit Optimization
Authors: Ilai Bistritz, Nicholas Bambos
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the numerical behavior of Algorithm 1 using a congestion game with N = 8 players and R = 4 resources. ... Figure 2: Congestion game with N = 8 players and R = 4 resources (a) Typical realization (b) Performance over 1000 Realizations |
| Researcher Affiliation | Academia | Ilai Bistritz, Nicholas Bambos Stanford University {bistritz,bambos}@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Cooperative Multi-player Bandit Optimization |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of source code for the described methodology. |
| Open Datasets | No | The paper describes a 'Numerical Example' using a 'congestion game' simulation environment, not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes a numerical simulation but does not provide specific details on dataset splits (e.g., train/validation/test percentages or counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only describing a 'Numerical Example' simulation. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | We used the step size sequence η (t) = 0.2 t0.9 and the sampling radius sequence δ (t) = 0.2 t0.1 . The memory was M = 6. Players broadcasted one reward value per turn, uniformly at random. |