Improved classical and quantum regret bounds for reinforcement learning with a generative model, including a new expected-regret measure under which quantum algorithms achieve polylogarithmic regret for infinite-horizon tasks.
Logarithmic online regret bounds for undiscounted reinforcement learning
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A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model
Improved classical and quantum regret bounds for reinforcement learning with a generative model, including a new expected-regret measure under which quantum algorithms achieve polylogarithmic regret for infinite-horizon tasks.