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20202026
most citedDamped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization

1 citations · 2 across the 7 of their papers we have counts for

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Showing 2021Show all

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math.OC2021

Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability

Yafei Wang, Bo Pan, Wei Tu +6

Sample average approximation (SAA), a popular method for tractably solving stochastic optimization problems, enjoys strong asymptotic performance guarantees in settings with indepe…

cs.LG2021★ 1 cited

Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization

Ke Sun, Yafei Wang, Yi Liu +5

Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL. Despite its…

cs.LG2021★ 1 cited

Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning

Ke Sun, Yingnan Zhao, Enze Shi +4

The remarkable empirical performance of distributional reinforcement learning (RL) has garnered increasing attention to understanding its theoretical advantages over classical RL.…