Cooperative Online Learning in Stochastic and Adversarial MDPs
Authors: Tal Lancewicki, Aviv Rosenberg, Yishay Mansour
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We thoroughly analyze all relevant settings, highlight the challenges and differences between the models, and prove nearly-matching regret lower and upper bounds. To our knowledge, we are the first to consider cooperative reinforcement learning (RL) with either non-fresh randomness or in adversarial MDPs. |
| Researcher Affiliation | Collaboration | 1Tel Aviv University 2Google Research, Tel Aviv. |
| Pseudocode | Yes | Algorithm 1 COOP-ULCAE |
| Open Source Code | No | No statements or links indicating the release of open-source code for the methodology described in this paper. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not mention using specific datasets or providing access information for them. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not mention dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not mention specific hardware used for experiments. |
| Software Dependencies | No | The paper focuses on theoretical analysis and algorithm design; it does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not describe a concrete experimental setup with hyperparameters or training settings. |