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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

ilde{O}(d\sqrt{DT})

with matching lower bound.

reinforcement learning MDP average-reward linear function approximation UCRL2-VTR

2021-02-15abs ↗pdf ↗