Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Authors: Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct several experiments using CE meta-solvers for JPSRO and demonstrate convergence on n-player, general-sum games. |
| Researcher Affiliation | Collaboration | 1Deep Mind 2University College London 3Universit e Gustave Eiffel. |
| Pseudocode | Yes | Algorithm 1 Two-Player PSRO ... Algorithm 2 JPSRO |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | All games used are available in Open Spiel (Lanctot et al., 2019). |
| Dataset Splits | No | The paper describes training multi-agent systems within game environments, but does not specify explicit training/validation/test dataset splits with percentages or sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper mentions software like CVXPY, OSQP, and Open Spiel, but does not provide specific version numbers for these dependencies, which are necessary for reproducible setup. |
| Experiment Setup | Yes | We use an exact BR oracle, and exactly evaluate policies in the meta-game by traversing the game tree to precisely isolate the MS s contribution to the algorithm. ... Random solvers were evaluated with five seeds and we plot the mean. ... Experiments were ran for up to 6 hours, after which they were terminated. |