We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| cyberagentailab/m2wu | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| load_utility_matrix | Ran | cyberagentailab/m2wu/runner/utils.py code served (permissive licence) · get_code("2c0b4215ef010cce") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper proposes Mutation-Driven Multiplicative Weights Update (M2WU) for learning an equilibrium in two-player zero-sum normal-form games and proves that it exhibits the last-iterate convergence property in both full and noisy feedback settings. In the former, players observe their exact gradient vectors of the utility functions. In the latter, they only observe the noisy gradient vectors. Even the celebrated Multiplicative Weights Update (MWU) and Optimistic MWU (OMWU) algorithms may not converge to a Nash equilibrium with noisy feedback. On the contrary, M2WU exhibits the last-iterate convergence to a stationary point near a Nash equilibrium in both feedback settings. We then prove that it converges to an exact Nash equilibrium by iteratively adapting the mutation term. We empirically confirm that M2WU outperforms MWU and OMWU in exploitability and convergence rates.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2208.09855")
get_code_for_paper("2208.09855")
have("2208.09855")
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