Credit Assignment For Collective Multiagent RL With Global Rewards
Authors: Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically our new approaches provide significantly better solutions than previous methods in the presence of global rewards on two real world problems modeling taxi fleet optimization and multiagent patrolling, and a synthetic grid navigation domain. Section 5 is titled Experiments and presents various performance metrics in figures. |
| Researcher Affiliation | Academia | Duc Thien Nguyen Akshat Kumar Hoong Chuin Lau School of Information Systems Singapore Management University 80 Stamford Road, Singapore 178902 {dtnguyen.2014,akshatkumar,hclau}@smu.edu.sg |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (no specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper mentions using real-world data for taxi demands and police incidents, but it does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions the learning framework and general aspects of the models (e.g., deep neural network for critic), but it does not provide specific experimental setup details such as concrete hyperparameter values (learning rate, batch size, number of epochs) or optimizer settings. |