Heterogeneous Multi-Agent Bandits with Parsimonious Hints
Authors: Amirmahdi Mirfakhar, Xuchuang Wang, Jinhang Zuo, Yair Zick, Mohammad Hajiesmaili
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we establish lower bounds to prove the optimality of our results and verify them through numerical simulations. We executed the algorithms HCLA, GP-HCLA, G-HCLA, HD-ETC, and EBHD-ETC with M = 4, K = 4, and match min 0.18, averaging regret and hint complexity over 50 replications for 105 rounds. |
| Researcher Affiliation | Academia | 1University of Massachusetts Amherst, 2City University of Hong Kong EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Hinted Centralized Learning Algorithm (HCLA) Algorithm 2: Generalized Projection-based Hinted Centralized Learning Algorithm (GP-HCLA) Algorithm 3: Hinted Decentralized Explore then Commit (HD-ETC) : agent m Algorithm 4: Elimination-Based Hinted Decentralized Explore then Commit (EBHD-ETC) : agent m |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of source code for the described methodology. |
| Open Datasets | No | We executed the algorithms HCLA, GP-HCLA, G-HCLA, HD-ETC, and EBHD-ETC with M = 4, K = 4, and match min 0.18, averaging regret and hint complexity over 50 replications for 105 rounds. The experiments appear to be based on simulated environments rather than a specific external dataset, and no dataset is mentioned as publicly available. |
| Dataset Splits | No | The paper does not use an external dataset, instead, it describes numerical simulations based on specified parameters (M, K, match min), so there are no dataset splits to provide. |
| Hardware Specification | No | The paper mentions numerical simulations and experiments, but does not provide any specific details about the hardware used to run these experiments. |
| Software Dependencies | No | The paper describes algorithms and numerical simulations but does not specify any software dependencies or their version numbers used for implementation or experimentation. |
| Experiment Setup | Yes | We executed the algorithms HCLA, GP-HCLA, G-HCLA, HD-ETC, and EBHD-ETC with M = 4, K = 4, and match min 0.18, averaging regret and hint complexity over 50 replications for 105 rounds. |