A multi-agent reinforcement learning model of common-pool resource appropriation
Authors: Julien Pérolat, Joel Z. Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments highlight the importance of trial-and-error learning in commonpool resource appropriation and shed light on the relationship between exclusion, sustainability, and inequality. |
| Researcher Affiliation | Collaboration | Julien Perolat Deep Mind London, UK perolat@google.com Joel Z. Leibo Deep Mind London, UK jzl@google.com Vinicius Zambaldi Deep Mind London, UK vzambaldi@google.com Charles Beattie Deep Mind London, UK cbeattie@google.com Karl Tuyls University of Liverpool Liverpool, UK karltuyls@google.com Thore Graepel Deep Mind London, UK thore@google.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks, nor any clearly labeled algorithm sections. |
| Open Source Code | No | The paper does not provide an unambiguous statement about releasing the source code for the methodology described, nor does it include a direct link to a code repository. |
| Open Datasets | No | The paper describes a custom-built simulation environment called 'The Commons Game' but does not provide concrete access information (link, DOI, repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes a reinforcement learning setup where agents learn in a simulated environment over episodes and training steps, but it does not specify explicit training/validation/test dataset splits or mention how the data generation process for reproduction would be partitioned. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions using Q-learning with function approximation (DQN) but does not provide specific software dependency details such as library names with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | The paper mentions environment parameters like 'time-out beam of length 10 and width 5' but does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) for the deep reinforcement learning agents. |