Protocols for Verifying Smooth Strategies in Bandits and Games
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Abstract (arXiv)
We study protocols for verifying approximate optimality of strategies in multi-armed bandits and normal-form games. As the number of actions available to each player is often large, we seek protocols where the number of queries to the utility oracle is sublinear in the number of actions. We prove that such verification is possible for sufficiently smooth strategies that do not put too much probability mass on any specific action. We provide protocols for verifying that a smooth policy for a multi-armed bandit is varepsilon-optimal. Our verification protocols require provably fewer arm queries than learning. Furthermore, we establish a nearly-tight lower bound on the query complexity of verification in our settings. As an application, we show how to use verification for bandits to achieve verification in normal-form games. This gives a protocol for verifying whether a given strategy profile is an approximate strong smooth Nash equilibrium, with a query complexity that is sublinear in the number of actions.
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References · 14
- 2016Optimal Best Arm Identification with Fixed Confidencecited by 239
- 2016Learning from Untrusted Datacited by 90
- 2018Adversarial Attacks on Stochastic Banditscited by 57
- 2021Corruption-Robust Offline Reinforcement Learningcited by 51
- 2016Settling the complexity of computing approximate two-player Nash equilibriacited by 31
- 2016Towards Instance Optimal Bounds for Best Arm Identificationcited by 30
- 2015On the Optimal Sample Complexity for Best Arm Identificationcited by 17
- 2013Learning Equilibria of Games via Payoff Queriescited by 8
- 2022PAC Verification of Statistical Algorithmscited by 8
- 2018Near-Optimal Communication Lower Bounds for Approximate Nash Equilibriacited by 6
- 2023Classical Verification of Quantum Learningcited by 6
- 2023Smooth Nash Equilibria: Algorithms and Complexitycited by 3
2 more
- 2024On the Power of Interactive Proofs for Learningcited by 3
- 2024Interactive proofs for verifying (quantum) learning and testingcited by 1
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