Is Learning in Games Good for the Learners?
Authors: William Brown, Jon Schneider, Kiran Vodrahalli
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider a number of questions related to tradeoffs between reward and regret in repeated gameplay between two agents. To facilitate this, we introduce a notion of generalized equilibrium which allows for asymmetric regret constraints, and yields polytopes of feasible values for each agent and pair of regret constraints, where we show that any such equilibrium is reachable by a pair of algorithms which maintain their regret guarantees against arbitrary opponents. |
| Researcher Affiliation | Collaboration | William Brown Columbia University w.brown@columbia.edu Jon Schneider Google Research jschnei@google.com Kiran Vodrahalli Google Research kirannv@google.com |
| Pseudocode | Yes | Algorithm 1 Rounded Mean-Based Doubling (RMBD) |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not mention the use of any specific datasets for training, validation, or testing, nor does it provide access information for any dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup or the specific hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any ancillary software or library dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe any specific experimental setup details such as hyperparameters or training configurations. |