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Achieving the first sub-Gaussian concentration bound for stochastic approximation under multiplicative noise could revolutionize how we approach stability in reinforcement learning algorithms.
Q-learning regret bounds can be achieved without optimism, but are highly sensitive to the suboptimality gap, motivating a new smoothed exploration strategy.
