Worst-Case Regret Bounds for Exploration via Randomized Value Functions

NeurIPS 2019 Daniel Russo

This paper studies a recent proposal to use randomized value functions to drive exploration in reinforcement learning. These randomized value functions are generated by injecting random noise into the training data, making the approach compatible with many popular methods for estimating parameterized value functions... (read more)

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