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1 result for “UCRL2-VTR”
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximationcs.LG

UCRL2-VTR achieves nearly optimal regret for learning MDPs with linear function approximation.

problem Learning infinite-horizon average-reward MDPs with linear function approximation.
method UCRL2-VTR algorithm with Bernstein-type bonus.
result Achieves a regret of ildeO(dDT) ilde{O}(d\sqrt{DT}) with matching lower bound.
reinforcement learningMDPaverage-rewardlinear function approximationUCRL2-VTR
2021-02-15abs ↗pdf ↗