Exploration-Exploitation Tradeoffs for Experts Algorithms in Reactive Environments

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Daniela Farias, Nimrod Megiddo


A reactive environment is one that responds to the actions of an agent rather than evolving obliviously. In reactive environments, experts algorithms must balance exploration and exploitation of experts more carefully than in oblivious ones. In addition, a more subtle definition of a learnable value of an expert is required. A general exploration-exploitation experts method is presented along with a proper definition of value. The method is shown to asymptotically perform as well as the best available expert. Several variants are analyzed from the viewpoint of the exploration-exploitation tradeoff, including explore-then-exploit, polynomially vanishing exploration, constant-frequency exploration, and constant-size explo- ration phases. Complexity and performance bounds are proven.