Research map / Bandits and online learning / Online convex optimization and games
Bandit Learning in Concave N-Person Games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use mirror descent with single-shot SPSA bandit feedback and suitably diminishing step sizes and query radii | No-regret learning in monotone games · Monotone concave games | equilibrium convergence: with probability 1 | Helped |
| Use bandit feedback instead of first-order oracle feedback | Mirror descent in strongly monotone games · Strongly monotone games | convergence rate: O(1/n) -> O(n^{-1/3}) | Hurt |
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